Just to review:

## [1] 27288
## [1] 27221
## [1] 11038
## [1] 11038
## [1] 9673

Below are the Overall patient characteristics.

Patient Characteristics Overall
variable_name level Overall
n 9673
micu (%) 1 9673 (100.0)
age (mean (sd)) 63.68 (17.94)
callout_month (%) 1 774 ( 8.0)
2 738 ( 7.6)
3 733 ( 7.6)
4 726 ( 7.5)
5 794 ( 8.2)
6 745 ( 7.7)
7 779 ( 8.1)
8 877 ( 9.1)
9 851 ( 8.8)
10 914 ( 9.4)
11 823 ( 8.5)
12 919 ( 9.5)
female (%) 0 4956 ( 51.2)
1 4717 ( 48.8)
request_tele (%) 0 6318 ( 65.3)
1 3355 ( 34.7)
request_resp (%) 0 9514 ( 98.4)
1 159 ( 1.6)
request_cdiff (%) 0 9190 ( 95.0)
1 483 ( 5.0)
request_mrsa (%) 0 8405 ( 86.9)
1 1268 ( 13.1)
request_vre (%) 0 9182 ( 94.9)
1 491 ( 5.1)
oasis (mean (sd)) 29.84 (7.30)
elixhauser_hospital (mean (sd)) 3.54 (7.28)
ethnicity (%) White 6910 ( 71.4)
African American/Black 1494 ( 15.4)
Other 1269 ( 13.1)
MED_SERVICE (%) FALSE 692 ( 7.2)
TRUE 8981 ( 92.8)
HOSP_FREE_DAYS (median [IQR]) 24.12 [20.29, 26.02]
callout_dayofweek (%) friday 1442 ( 14.9)
monday 1300 ( 13.4)
saturday 1292 ( 13.4)
sunday 1267 ( 13.1)
thursday 1412 ( 14.6)
tuesday 1422 ( 14.7)
wednesday 1538 ( 15.9)
CALLOUT_DURING_NIGHT (%) FALSE 9607 ( 99.3)
TRUE 66 ( 0.7)
CALLOUT_DURING_ROUNDS (%) FALSE 3816 ( 39.5)
TRUE 5857 ( 60.5)
DISCHARGEDELAY_HOURS (mean (sd)) 10.37 (10.28)
hourofcallout2 (median [IQR]) 11.40 [10.12, 13.17]
PROPFULL_BEDS (mean (sd)) 0.91 (0.09)
postcalldaycat2 (%) 0 7800 ( 80.6)
[1,5] 1873 ( 19.4)
hospitaldeath (%) 0 9140 ( 94.5)
1 533 ( 5.5)
los_preicu_days (median [IQR]) 0.00 [0.00, 0.15]
los_post_callout_days (median [IQR]) 4.16 [2.28, 7.24]
los_post_icu_days (median [IQR]) 3.80 [1.95, 6.89]
los_pre_callout_days (median [IQR]) 1.77 [0.94, 3.68]
callout_year (%) 2005 376 ( 3.9)
2006 1070 ( 11.1)
2007 1417 ( 14.6)
2008 1638 ( 16.9)
2009 1668 ( 17.2)
2010 1739 ( 18.0)
2011 1765 ( 18.2)

Question 1: Who has long discharge delays?

Patient Characteristics By Discharge Delay Categories (Hours)
variable_name level [ 0, 4) [ 4, 8) [ 8, 24) [ 24,130] p test
n 1308 4348 3006 1011
micu (%) 1 1308 (100.0) 4348 (100.0) 3006 (100.0) 1011 (100.0) NA
age (mean (sd)) 63.11 (17.86) 63.51 (17.92) 64.04 (18.02) 64.12 (17.91) 0.309
callout_month (%) 1 113 ( 8.6) 334 ( 7.7) 238 ( 7.9) 89 ( 8.8) <0.001
2 95 ( 7.3) 296 ( 6.8) 257 ( 8.5) 90 ( 8.9)
3 105 ( 8.0) 365 ( 8.4) 212 ( 7.1) 51 ( 5.0)
4 124 ( 9.5) 354 ( 8.1) 193 ( 6.4) 55 ( 5.4)
5 103 ( 7.9) 374 ( 8.6) 251 ( 8.3) 66 ( 6.5)
6 110 ( 8.4) 347 ( 8.0) 214 ( 7.1) 74 ( 7.3)
7 104 ( 8.0) 371 ( 8.5) 233 ( 7.8) 71 ( 7.0)
8 99 ( 7.6) 379 ( 8.7) 290 ( 9.6) 109 ( 10.8)
9 105 ( 8.0) 328 ( 7.5) 292 ( 9.7) 126 ( 12.5)
10 112 ( 8.6) 379 ( 8.7) 309 ( 10.3) 114 ( 11.3)
11 100 ( 7.6) 359 ( 8.3) 257 ( 8.5) 107 ( 10.6)
12 138 ( 10.6) 462 ( 10.6) 260 ( 8.6) 59 ( 5.8)
female (%) 0 682 ( 52.1) 2205 ( 50.7) 1523 ( 50.7) 546 ( 54.0) 0.221
1 626 ( 47.9) 2143 ( 49.3) 1483 ( 49.3) 465 ( 46.0)
request_tele (%) 0 808 ( 61.8) 2836 ( 65.2) 2004 ( 66.7) 670 ( 66.3) 0.018
1 500 ( 38.2) 1512 ( 34.8) 1002 ( 33.3) 341 ( 33.7)
request_resp (%) 0 1292 ( 98.8) 4274 ( 98.3) 2955 ( 98.3) 993 ( 98.2) 0.639
1 16 ( 1.2) 74 ( 1.7) 51 ( 1.7) 18 ( 1.8)
request_cdiff (%) 0 1257 ( 96.1) 4139 ( 95.2) 2854 ( 94.9) 940 ( 93.0) 0.006
1 51 ( 3.9) 209 ( 4.8) 152 ( 5.1) 71 ( 7.0)
request_mrsa (%) 0 1184 ( 90.5) 3804 ( 87.5) 2588 ( 86.1) 829 ( 82.0) <0.001
1 124 ( 9.5) 544 ( 12.5) 418 ( 13.9) 182 ( 18.0)
request_vre (%) 0 1235 ( 94.4) 4144 ( 95.3) 2860 ( 95.1) 943 ( 93.3) 0.045
1 73 ( 5.6) 204 ( 4.7) 146 ( 4.9) 68 ( 6.7)
oasis (mean (sd)) 29.38 (7.22) 29.78 (7.37) 30.01 (7.15) 30.21 (7.49) 0.021
elixhauser_hospital (mean (sd)) 3.30 (7.12) 3.49 (7.23) 3.52 (7.35) 4.15 (7.45) 0.031
ethnicity (%) White 962 ( 73.5) 3107 ( 71.5) 2114 ( 70.3) 727 ( 71.9) 0.534
African American/Black 190 ( 14.5) 673 ( 15.5) 481 ( 16.0) 150 ( 14.8)
Other 156 ( 11.9) 568 ( 13.1) 411 ( 13.7) 134 ( 13.3)
MED_SERVICE (%) FALSE 155 ( 11.9) 327 ( 7.5) 156 ( 5.2) 54 ( 5.3) <0.001
TRUE 1153 ( 88.1) 4021 ( 92.5) 2850 ( 94.8) 957 ( 94.7)
HOSP_FREE_DAYS (median [IQR]) 24.05 [20.72, 25.98] 24.05 [20.18, 25.97] 24.25 [21.03, 26.09] 24.16 [19.63, 26.23] <0.001 nonnorm
callout_dayofweek (%) friday 165 ( 12.6) 641 ( 14.7) 480 ( 16.0) 156 ( 15.4) <0.001
monday 208 ( 15.9) 612 ( 14.1) 370 ( 12.3) 110 ( 10.9)
saturday 146 ( 11.2) 594 ( 13.7) 442 ( 14.7) 110 ( 10.9)
sunday 234 ( 17.9) 611 ( 14.1) 326 ( 10.8) 96 ( 9.5)
thursday 172 ( 13.1) 580 ( 13.3) 467 ( 15.5) 193 ( 19.1)
tuesday 185 ( 14.1) 618 ( 14.2) 450 ( 15.0) 169 ( 16.7)
wednesday 198 ( 15.1) 692 ( 15.9) 471 ( 15.7) 177 ( 17.5)
CALLOUT_DURING_NIGHT (%) FALSE 1293 ( 98.9) 4331 ( 99.6) 2976 ( 99.0) 1007 ( 99.6) 0.002
TRUE 15 ( 1.1) 17 ( 0.4) 30 ( 1.0) 4 ( 0.4)
CALLOUT_DURING_ROUNDS (%) FALSE 765 ( 58.5) 1744 ( 40.1) 862 ( 28.7) 445 ( 44.0) <0.001
TRUE 543 ( 41.5) 2604 ( 59.9) 2144 ( 71.3) 566 ( 56.0)
DISCHARGEDELAY_HOURS (mean (sd)) 3.07 (0.70) 5.95 (1.11) 11.40 (3.49) 35.80 (12.87) <0.001
hourofcallout2 (median [IQR]) 12.60 [10.71, 15.25] 11.52 [10.35, 13.07] 10.72 [9.47, 12.17] 11.60 [10.39, 13.88] <0.001 nonnorm
PROPFULL_BEDS (mean (sd)) 0.89 (0.09) 0.90 (0.09) 0.92 (0.08) 0.95 (0.07) <0.001
postcalldaycat2 (%) 0 1284 ( 98.2) 4211 ( 96.8) 2305 ( 76.7) 0 ( 0.0) <0.001
[1,5] 24 ( 1.8) 137 ( 3.2) 701 ( 23.3) 1011 (100.0)
hospitaldeath (%) 0 1233 ( 94.3) 4117 ( 94.7) 2848 ( 94.7) 942 ( 93.2) 0.243
1 75 ( 5.7) 231 ( 5.3) 158 ( 5.3) 69 ( 6.8)
los_preicu_days (median [IQR]) 0.00 [0.00, 0.38] 0.00 [0.00, 0.15] 0.00 [0.00, 0.10] 0.00 [0.00, 0.07] 0.012 nonnorm
los_post_callout_days (median [IQR]) 3.97 [2.11, 6.99] 4.14 [2.26, 7.18] 4.14 [2.31, 7.17] 5.13 [3.11, 9.06] <0.001 nonnorm
los_post_icu_days (median [IQR]) 3.83 [1.97, 6.85] 3.89 [2.00, 6.94] 3.67 [1.88, 6.71] 3.71 [1.68, 7.33] <0.001 nonnorm
los_pre_callout_days (median [IQR]) 1.80 [0.98, 3.57] 1.78 [0.95, 3.72] 1.76 [0.89, 3.62] 1.73 [0.88, 3.76] 0.391 nonnorm
callout_year (%) 2005 27 ( 2.1) 164 ( 3.8) 113 ( 3.8) 72 ( 7.1) <0.001
2006 102 ( 7.8) 404 ( 9.3) 297 ( 9.9) 267 ( 26.4)
2007 140 ( 10.7) 521 ( 12.0) 474 ( 15.8) 282 ( 27.9)
2008 229 ( 17.5) 786 ( 18.1) 516 ( 17.2) 107 ( 10.6)
2009 308 ( 23.5) 811 ( 18.7) 475 ( 15.8) 74 ( 7.3)
2010 222 ( 17.0) 779 ( 17.9) 608 ( 20.2) 130 ( 12.9)
2011 280 ( 21.4) 883 ( 20.3) 523 ( 17.4) 79 ( 7.8)

Determinants of the DD are quite complex, depending on many factors. We instead focus on breaking down DD to >=24 vs <24h

Patient Characteristics By Discharge Delay Categories (Hours)
variable_name level [ 0.238, 24.000) [ 24.000,129.566] p test
n 8662 1011
micu (%) 1 8662 (100.0) 1011 (100.0) NA
age (mean (sd)) 63.63 (17.95) 64.12 (17.91) 0.414
callout_month (%) 1 685 ( 7.9) 89 ( 8.8) <0.001
2 648 ( 7.5) 90 ( 8.9)
3 682 ( 7.9) 51 ( 5.0)
4 671 ( 7.7) 55 ( 5.4)
5 728 ( 8.4) 66 ( 6.5)
6 671 ( 7.7) 74 ( 7.3)
7 708 ( 8.2) 71 ( 7.0)
8 768 ( 8.9) 109 ( 10.8)
9 725 ( 8.4) 126 ( 12.5)
10 800 ( 9.2) 114 ( 11.3)
11 716 ( 8.3) 107 ( 10.6)
12 860 ( 9.9) 59 ( 5.8)
female (%) 0 4410 ( 50.9) 546 ( 54.0) 0.067
1 4252 ( 49.1) 465 ( 46.0)
request_tele (%) 0 5648 ( 65.2) 670 ( 66.3) 0.523
1 3014 ( 34.8) 341 ( 33.7)
request_resp (%) 0 8521 ( 98.4) 993 ( 98.2) 0.818
1 141 ( 1.6) 18 ( 1.8)
request_cdiff (%) 0 8250 ( 95.2) 940 ( 93.0) 0.002
1 412 ( 4.8) 71 ( 7.0)
request_mrsa (%) 0 7576 ( 87.5) 829 ( 82.0) <0.001
1 1086 ( 12.5) 182 ( 18.0)
request_vre (%) 0 8239 ( 95.1) 943 ( 93.3) 0.014
1 423 ( 4.9) 68 ( 6.7)
oasis (mean (sd)) 29.80 (7.27) 30.21 (7.49) 0.085
elixhauser_hospital (mean (sd)) 3.47 (7.25) 4.15 (7.45) 0.005
ethnicity (%) White 6183 ( 71.4) 727 ( 71.9) 0.851
African American/Black 1344 ( 15.5) 150 ( 14.8)
Other 1135 ( 13.1) 134 ( 13.3)
MED_SERVICE (%) FALSE 638 ( 7.4) 54 ( 5.3) 0.022
TRUE 8024 ( 92.6) 957 ( 94.7)
HOSP_FREE_DAYS (median [IQR]) 24.11 [20.35, 26.01] 24.16 [19.63, 26.23] 0.035 nonnorm
callout_dayofweek (%) friday 1286 ( 14.8) 156 ( 15.4) <0.001
monday 1190 ( 13.7) 110 ( 10.9)
saturday 1182 ( 13.6) 110 ( 10.9)
sunday 1171 ( 13.5) 96 ( 9.5)
thursday 1219 ( 14.1) 193 ( 19.1)
tuesday 1253 ( 14.5) 169 ( 16.7)
wednesday 1361 ( 15.7) 177 ( 17.5)
CALLOUT_DURING_NIGHT (%) FALSE 8600 ( 99.3) 1007 ( 99.6) 0.333
TRUE 62 ( 0.7) 4 ( 0.4)
CALLOUT_DURING_ROUNDS (%) FALSE 3371 ( 38.9) 445 ( 44.0) 0.002
TRUE 5291 ( 61.1) 566 ( 56.0)
DISCHARGEDELAY_HOURS (mean (sd)) 7.40 (3.79) 35.80 (12.87) <0.001
hourofcallout2 (median [IQR]) 11.37 [10.08, 13.08] 11.60 [10.39, 13.88] <0.001 nonnorm
PROPFULL_BEDS (mean (sd)) 0.91 (0.09) 0.95 (0.07) <0.001
postcalldaycat2 (%) 0 7800 ( 90.0) 0 ( 0.0) <0.001
[1,5] 862 ( 10.0) 1011 (100.0)
hospitaldeath (%) 0 8198 ( 94.6) 942 ( 93.2) 0.062
1 464 ( 5.4) 69 ( 6.8)
los_preicu_days (median [IQR]) 0.00 [0.00, 0.16] 0.00 [0.00, 0.07] 0.459 nonnorm
los_post_callout_days (median [IQR]) 4.12 [2.26, 7.15] 5.13 [3.11, 9.06] <0.001 nonnorm
los_post_icu_days (median [IQR]) 3.81 [1.97, 6.86] 3.71 [1.68, 7.33] 0.012 nonnorm
los_pre_callout_days (median [IQR]) 1.77 [0.94, 3.68] 1.73 [0.88, 3.76] 0.541 nonnorm
callout_year (%) 2005 304 ( 3.5) 72 ( 7.1) <0.001
2006 803 ( 9.3) 267 ( 26.4)
2007 1135 ( 13.1) 282 ( 27.9)
2008 1531 ( 17.7) 107 ( 10.6)
2009 1594 ( 18.4) 74 ( 7.3)
2010 1609 ( 18.6) 130 ( 12.9)
2011 1686 ( 19.5) 79 ( 7.8)

We fit a logistic regression model for DD>24 as the “outcome” with:

  1. Demographics: age, sex,
  2. Requests: tele, resp, mrsa, cdiff, vre
  3. Adjustment for severity/comoribidity/icu los: oasis, exlixhauser, los_pre_callout_days
  4. Possible structural/admin variables: DOW, month, year.
  5. Census variables: type of bed requested, proportion of hospital beds is use.

as covariates.

Model selection: fit full model, reduce backwards stepwise, until all variables are significant by LRT.

AIC/BIC model selection was done first, but used only as a comparison to the manually worked selection (jdr.ddelay.glm).

## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis, 
##     g = 3) + cut2(age, g = 3) + female + request_tele + request_resp + 
##     request_mrsa + request_vre + request_cdiff + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9, 
##         1)) + MED_SERVICE + cut2(hourofcallout2, c(7, 12, 19))
##                                                               Df Deviance
## <none>                                                             5521.7
## cut2(oasis, g = 3)                                             2   5525.3
## cut2(age, g = 3)                                               2   5522.4
## female                                                         1   5527.5
## request_tele                                                   1   5523.3
## request_resp                                                   1   5522.7
## request_mrsa                                                   1   5544.2
## request_vre                                                    1   5526.1
## request_cdiff                                                  1   5532.7
## cut2(elixhauser_hospital, g = 3)                               2   5522.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     4   5530.0
## as.factor(callout_month)                                      11   5586.8
## as.factor(callout_year)                                        6   6077.5
## as.factor(callout_dayofweek)                                   6   5580.4
## MED_SERVICE                                                    1   5522.4
## cut2(hourofcallout2, c(7, 12, 19))                             3   5539.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  2   5534.5
##                                                                  AIC
## <none>                                                        5619.7
## cut2(oasis, g = 3)                                            5619.3
## cut2(age, g = 3)                                              5616.4
## female                                                        5623.5
## request_tele                                                  5619.3
## request_resp                                                  5618.7
## request_mrsa                                                  5640.2
## request_vre                                                   5622.1
## request_cdiff                                                 5628.7
## cut2(elixhauser_hospital, g = 3)                              5616.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    5620.0
## as.factor(callout_month)                                      5662.8
## as.factor(callout_year)                                       6163.5
## as.factor(callout_dayofweek)                                  5666.4
## MED_SERVICE                                                   5618.4
## cut2(hourofcallout2, c(7, 12, 19))                            5631.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5628.5
##                                                                  LRT
## <none>                                                              
## cut2(oasis, g = 3)                                              3.61
## cut2(age, g = 3)                                                0.68
## female                                                          5.80
## request_tele                                                    1.53
## request_resp                                                    0.98
## request_mrsa                                                   22.48
## request_vre                                                     4.35
## request_cdiff                                                  10.98
## cut2(elixhauser_hospital, g = 3)                                0.86
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      8.24
## as.factor(callout_month)                                       65.11
## as.factor(callout_year)                                       555.79
## as.factor(callout_dayofweek)                                   58.67
## MED_SERVICE                                                     0.68
## cut2(hourofcallout2, c(7, 12, 19))                             17.93
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  12.77
##                                                                Pr(>Chi)
## <none>                                                                 
## cut2(oasis, g = 3)                                            0.1643833
## cut2(age, g = 3)                                              0.7131505
## female                                                        0.0160151
## request_tele                                                  0.2162413
## request_resp                                                  0.3232036
## request_mrsa                                                  2.127e-06
## request_vre                                                   0.0370250
## request_cdiff                                                 0.0009210
## cut2(elixhauser_hospital, g = 3)                              0.6506463
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    0.0833378
## as.factor(callout_month)                                      1.029e-09
## as.factor(callout_year)                                       < 2.2e-16
## as.factor(callout_dayofweek)                                  8.366e-11
## MED_SERVICE                                                   0.4094765
## cut2(hourofcallout2, c(7, 12, 19))                            0.0004549
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0016855
##                                                                  
## <none>                                                           
## cut2(oasis, g = 3)                                               
## cut2(age, g = 3)                                                 
## female                                                        *  
## request_tele                                                     
## request_resp                                                     
## request_mrsa                                                  ***
## request_vre                                                   *  
## request_cdiff                                                 ***
## cut2(elixhauser_hospital, g = 3)                                 
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    .  
## as.factor(callout_month)                                      ***
## as.factor(callout_year)                                       ***
## as.factor(callout_dayofweek)                                  ***
## MED_SERVICE                                                      
## cut2(hourofcallout2, c(7, 12, 19))                            ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ request_mrsa + 
##     request_cdiff + as.factor(callout_year) + cut2(PROPFULL_BEDS, 
##     c(0.9, 1))
##                                Df Deviance    AIC    LRT  Pr(>Chi)    
## <none>                              5701.7 5723.7                     
## request_mrsa                    1   5730.9 5750.9  29.17 6.616e-08 ***
## request_cdiff                   1   5714.5 5734.5  12.81 0.0003452 ***
## as.factor(callout_year)         6   6241.0 6251.0 539.24 < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))  2   5909.1 5927.1 207.43 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis, 
##     g = 3) + female + request_mrsa + request_vre + request_cdiff + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + cut2(hourofcallout2, c(7, 12, 19)) + as.factor(callout_wardid == 
##     1):cut2(PROPFULL_BEDS, c(0.9, 1))
##                                                               Df Deviance
## <none>                                                             5526.4
## cut2(oasis, g = 3)                                             2   5530.8
## female                                                         1   5532.9
## request_mrsa                                                   1   5549.5
## request_vre                                                    1   5531.1
## request_cdiff                                                  1   5537.7
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     4   5534.6
## as.factor(callout_month)                                      11   5591.2
## as.factor(callout_year)                                        6   6085.3
## as.factor(callout_dayofweek)                                   6   5585.0
## cut2(hourofcallout2, c(7, 12, 19))                             3   5544.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  2   5539.4
##                                                                  AIC
## <none>                                                        5610.4
## cut2(oasis, g = 3)                                            5610.8
## female                                                        5614.9
## request_mrsa                                                  5631.5
## request_vre                                                   5613.1
## request_cdiff                                                 5619.7
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    5610.6
## as.factor(callout_month)                                      5653.2
## as.factor(callout_year)                                       6157.3
## as.factor(callout_dayofweek)                                  5657.0
## cut2(hourofcallout2, c(7, 12, 19))                            5622.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5619.4
##                                                                  LRT
## <none>                                                              
## cut2(oasis, g = 3)                                              4.38
## female                                                          6.51
## request_mrsa                                                   23.08
## request_vre                                                     4.65
## request_cdiff                                                  11.27
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      8.17
## as.factor(callout_month)                                       64.80
## as.factor(callout_year)                                       558.90
## as.factor(callout_dayofweek)                                   58.61
## cut2(hourofcallout2, c(7, 12, 19))                             17.88
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  12.95
##                                                                Pr(>Chi)
## <none>                                                                 
## cut2(oasis, g = 3)                                            0.1121047
## female                                                        0.0107553
## request_mrsa                                                  1.557e-06
## request_vre                                                   0.0310622
## request_cdiff                                                 0.0007885
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    0.0855801
## as.factor(callout_month)                                      1.173e-09
## as.factor(callout_year)                                       < 2.2e-16
## as.factor(callout_dayofweek)                                  8.628e-11
## cut2(hourofcallout2, c(7, 12, 19))                            0.0004660
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0015379
##                                                                  
## <none>                                                           
## cut2(oasis, g = 3)                                               
## female                                                        *  
## request_mrsa                                                  ***
## request_vre                                                   *  
## request_cdiff                                                 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    .  
## as.factor(callout_month)                                      ***
## as.factor(callout_year)                                       ***
## as.factor(callout_dayofweek)                                  ***
## cut2(hourofcallout2, c(7, 12, 19))                            ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis, 
##     g = 3) + cut2(age, g = 3) + female + request_tele + request_resp + 
##     request_mrsa + request_vre + request_cdiff + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9, 
##         1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 
##     12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5521.7
## cut2(oasis, g = 3)                                              2   5525.3
## cut2(age, g = 3)                                                2   5522.4
## female                                                          1   5527.5
## request_tele                                                    1   5523.3
## request_resp                                                    1   5522.7
## request_mrsa                                                    1   5544.2
## request_vre                                                     1   5526.1
## request_cdiff                                                   1   5532.7
## cut2(elixhauser_hospital, g = 3)                                2   5522.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5530.0
## as.factor(callout_month)                                       11   5586.8
## as.factor(callout_year)                                         6   6077.5
## as.factor(callout_dayofweek)                                    6   5580.4
## MED_SERVICE                                                     1   5522.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5539.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5534.5
##                                                                   AIC
## <none>                                                         5619.7
## cut2(oasis, g = 3)                                             5619.3
## cut2(age, g = 3)                                               5616.4
## female                                                         5623.5
## request_tele                                                   5619.3
## request_resp                                                   5618.7
## request_mrsa                                                   5640.2
## request_vre                                                    5622.1
## request_cdiff                                                  5628.7
## cut2(elixhauser_hospital, g = 3)                               5616.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5620.0
## as.factor(callout_month)                                       5662.8
## as.factor(callout_year)                                        6163.5
## as.factor(callout_dayofweek)                                   5666.4
## MED_SERVICE                                                    5618.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5631.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5628.5
##                                                                   LRT
## <none>                                                               
## cut2(oasis, g = 3)                                               3.61
## cut2(age, g = 3)                                                 0.68
## female                                                           5.80
## request_tele                                                     1.53
## request_resp                                                     0.98
## request_mrsa                                                    22.48
## request_vre                                                      4.35
## request_cdiff                                                   10.98
## cut2(elixhauser_hospital, g = 3)                                 0.86
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                       8.24
## as.factor(callout_month)                                        65.11
## as.factor(callout_year)                                        555.79
## as.factor(callout_dayofweek)                                    58.67
## MED_SERVICE                                                      0.68
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  17.93
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   12.77
##                                                                 Pr(>Chi)
## <none>                                                                  
## cut2(oasis, g = 3)                                             0.1643833
## cut2(age, g = 3)                                               0.7131505
## female                                                         0.0160151
## request_tele                                                   0.2162413
## request_resp                                                   0.3232036
## request_mrsa                                                   2.127e-06
## request_vre                                                    0.0370250
## request_cdiff                                                  0.0009210
## cut2(elixhauser_hospital, g = 3)                               0.6506463
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     0.0833378
## as.factor(callout_month)                                       1.029e-09
## as.factor(callout_year)                                        < 2.2e-16
## as.factor(callout_dayofweek)                                   8.366e-11
## MED_SERVICE                                                    0.4094765
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004549
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.0016855
##                                                                   
## <none>                                                            
## cut2(oasis, g = 3)                                                
## cut2(age, g = 3)                                                  
## female                                                         *  
## request_tele                                                      
## request_resp                                                      
## request_mrsa                                                   ***
## request_vre                                                    *  
## request_cdiff                                                  ***
## cut2(elixhauser_hospital, g = 3)                                  
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     .  
## as.factor(callout_month)                                       ***
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                   ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(age, g = 3)"
## [1] 0.7131505
## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis, 
##     g = 3) + female + request_tele + request_resp + request_mrsa + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, 
##     c(0.9, 1))
##                                                                Df Deviance
## <none>                                                              5522.4
## cut2(oasis, g = 3)                                              2   5525.8
## female                                                          1   5528.4
## request_tele                                                    1   5523.9
## request_resp                                                    1   5523.4
## request_mrsa                                                    1   5545.0
## request_vre                                                     1   5527.0
## request_cdiff                                                   1   5533.3
## cut2(elixhauser_hospital, g = 3)                                2   5523.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5530.7
## as.factor(callout_month)                                       11   5587.9
## as.factor(callout_year)                                         6   6077.9
## as.factor(callout_dayofweek)                                    6   5581.1
## MED_SERVICE                                                     1   5523.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5540.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5535.3
##                                                                   AIC
## <none>                                                         5616.4
## cut2(oasis, g = 3)                                             5615.8
## female                                                         5620.4
## request_tele                                                   5615.9
## request_resp                                                   5615.4
## request_mrsa                                                   5637.0
## request_vre                                                    5619.0
## request_cdiff                                                  5625.3
## cut2(elixhauser_hospital, g = 3)                               5613.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5616.7
## as.factor(callout_month)                                       5659.9
## as.factor(callout_year)                                        6159.9
## as.factor(callout_dayofweek)                                   5663.1
## MED_SERVICE                                                    5615.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5628.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5625.3
##                                                                   LRT
## <none>                                                               
## cut2(oasis, g = 3)                                               3.40
## female                                                           5.96
## request_tele                                                     1.48
## request_resp                                                     1.03
## request_mrsa                                                    22.61
## request_vre                                                      4.58
## request_cdiff                                                   10.92
## cut2(elixhauser_hospital, g = 3)                                 0.73
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                       8.28
## as.factor(callout_month)                                        65.45
## as.factor(callout_year)                                        555.54
## as.factor(callout_dayofweek)                                    58.67
## MED_SERVICE                                                      0.71
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  17.89
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   12.88
##                                                                 Pr(>Chi)
## <none>                                                                  
## cut2(oasis, g = 3)                                             0.1828121
## female                                                         0.0146237
## request_tele                                                   0.2232544
## request_resp                                                   0.3109412
## request_mrsa                                                   1.988e-06
## request_vre                                                    0.0323407
## request_cdiff                                                  0.0009537
## cut2(elixhauser_hospital, g = 3)                               0.6940781
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     0.0819099
## as.factor(callout_month)                                       8.878e-10
## as.factor(callout_year)                                        < 2.2e-16
## as.factor(callout_dayofweek)                                   8.397e-11
## MED_SERVICE                                                    0.3985511
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004629
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.0015927
##                                                                   
## <none>                                                            
## cut2(oasis, g = 3)                                                
## female                                                         *  
## request_tele                                                      
## request_resp                                                      
## request_mrsa                                                   ***
## request_vre                                                    *  
## request_cdiff                                                  ***
## cut2(elixhauser_hospital, g = 3)                                  
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     .  
## as.factor(callout_month)                                       ***
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                   ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(elixhauser_hospital, g = 3)"
## [1] 0.6940781
## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis, 
##     g = 3) + female + request_tele + request_resp + request_mrsa + 
##     request_vre + request_cdiff + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 
##     1):cut2(PROPFULL_BEDS, c(0.9, 1))
##                                                                Df Deviance
## <none>                                                              5523.1
## cut2(oasis, g = 3)                                              2   5527.1
## female                                                          1   5529.5
## request_tele                                                    1   5524.8
## request_resp                                                    1   5524.2
## request_mrsa                                                    1   5545.8
## request_vre                                                     1   5527.9
## request_cdiff                                                   1   5534.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5531.2
## as.factor(callout_month)                                       11   5588.7
## as.factor(callout_year)                                         6   6080.9
## as.factor(callout_dayofweek)                                    6   5581.8
## MED_SERVICE                                                     1   5523.9
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5541.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5535.9
##                                                                   AIC
## <none>                                                         5613.1
## cut2(oasis, g = 3)                                             5613.1
## female                                                         5617.5
## request_tele                                                   5612.8
## request_resp                                                   5612.2
## request_mrsa                                                   5633.8
## request_vre                                                    5615.9
## request_cdiff                                                  5622.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5613.2
## as.factor(callout_month)                                       5656.7
## as.factor(callout_year)                                        6158.9
## as.factor(callout_dayofweek)                                   5659.8
## MED_SERVICE                                                    5611.9
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5625.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5621.9
##                                                                   LRT
## <none>                                                               
## cut2(oasis, g = 3)                                               3.97
## female                                                           6.34
## request_tele                                                     1.62
## request_resp                                                     1.02
## request_mrsa                                                    22.63
## request_vre                                                      4.74
## request_cdiff                                                   11.17
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                       8.07
## as.factor(callout_month)                                        65.53
## as.factor(callout_year)                                        557.72
## as.factor(callout_dayofweek)                                    58.71
## MED_SERVICE                                                      0.79
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  18.12
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   12.76
##                                                                 Pr(>Chi)
## <none>                                                                  
## cut2(oasis, g = 3)                                             0.1370424
## female                                                         0.0117737
## request_tele                                                   0.2032677
## request_resp                                                   0.3130016
## request_mrsa                                                   1.966e-06
## request_vre                                                    0.0295421
## request_cdiff                                                  0.0008301
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     0.0889496
## as.factor(callout_month)                                       8.567e-10
## as.factor(callout_year)                                        < 2.2e-16
## as.factor(callout_dayofweek)                                   8.222e-11
## MED_SERVICE                                                    0.3744853
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004164
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.0016993
##                                                                   
## <none>                                                            
## cut2(oasis, g = 3)                                                
## female                                                         *  
## request_tele                                                      
## request_resp                                                      
## request_mrsa                                                   ***
## request_vre                                                    *  
## request_cdiff                                                  ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     .  
## as.factor(callout_month)                                       ***
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                   ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "MED_SERVICE"
## [1] 0.3744853
## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis, 
##     g = 3) + female + request_tele + request_resp + request_mrsa + 
##     request_vre + request_cdiff + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 
##     1):cut2(PROPFULL_BEDS, c(0.9, 1))
##                                                                Df Deviance
## <none>                                                              5523.9
## cut2(oasis, g = 3)                                              2   5528.1
## female                                                          1   5530.3
## request_tele                                                    1   5525.5
## request_resp                                                    1   5525.0
## request_mrsa                                                    1   5546.9
## request_vre                                                     1   5528.7
## request_cdiff                                                   1   5535.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5532.0
## as.factor(callout_month)                                       11   5589.5
## as.factor(callout_year)                                         6   6084.6
## as.factor(callout_dayofweek)                                    6   5582.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5541.9
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5536.8
##                                                                   AIC
## <none>                                                         5611.9
## cut2(oasis, g = 3)                                             5612.1
## female                                                         5616.3
## request_tele                                                   5611.5
## request_resp                                                   5611.0
## request_mrsa                                                   5632.9
## request_vre                                                    5614.7
## request_cdiff                                                  5621.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5612.0
## as.factor(callout_month)                                       5655.5
## as.factor(callout_year)                                        6160.6
## as.factor(callout_dayofweek)                                   5658.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5623.9
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5620.8
##                                                                   LRT
## <none>                                                               
## cut2(oasis, g = 3)                                               4.22
## female                                                           6.33
## request_tele                                                     1.53
## request_resp                                                     1.05
## request_mrsa                                                    23.00
## request_vre                                                      4.80
## request_cdiff                                                   11.27
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                       8.12
## as.factor(callout_month)                                        65.57
## as.factor(callout_year)                                        560.70
## as.factor(callout_dayofweek)                                    58.67
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  18.01
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   12.92
##                                                                 Pr(>Chi)
## <none>                                                                  
## cut2(oasis, g = 3)                                             0.1211694
## female                                                         0.0118413
## request_tele                                                   0.2159673
## request_resp                                                   0.3050004
## request_mrsa                                                   1.619e-06
## request_vre                                                    0.0284797
## request_cdiff                                                  0.0007868
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     0.0871798
## as.factor(callout_month)                                       8.432e-10
## as.factor(callout_year)                                        < 2.2e-16
## as.factor(callout_dayofweek)                                   8.371e-11
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004371
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.0015647
##                                                                   
## <none>                                                            
## cut2(oasis, g = 3)                                                
## female                                                         *  
## request_tele                                                      
## request_resp                                                      
## request_mrsa                                                   ***
## request_vre                                                    *  
## request_cdiff                                                  ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     .  
## as.factor(callout_month)                                       ***
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                   ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_resp"
## [1] 0.3050004
## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis, 
##     g = 3) + female + request_tele + request_mrsa + request_vre + 
##     request_cdiff + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + 
##     as.factor(callout_month) + as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") + 
##     as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 
##         1))
##                                                                Df Deviance
## <none>                                                              5525.0
## cut2(oasis, g = 3)                                              2   5529.2
## female                                                          1   5531.4
## request_tele                                                    1   5526.4
## request_mrsa                                                    1   5548.1
## request_vre                                                     1   5529.7
## request_cdiff                                                   1   5536.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5533.2
## as.factor(callout_month)                                       11   5590.0
## as.factor(callout_year)                                         6   6084.9
## as.factor(callout_dayofweek)                                    6   5583.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5542.8
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5537.9
##                                                                   AIC
## <none>                                                         5611.0
## cut2(oasis, g = 3)                                             5611.2
## female                                                         5615.4
## request_tele                                                   5610.4
## request_mrsa                                                   5632.1
## request_vre                                                    5613.7
## request_cdiff                                                  5620.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5611.2
## as.factor(callout_month)                                       5654.0
## as.factor(callout_year)                                        6158.9
## as.factor(callout_dayofweek)                                   5657.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5622.8
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5619.9
##                                                                   LRT
## <none>                                                               
## cut2(oasis, g = 3)                                               4.21
## female                                                           6.39
## request_tele                                                     1.46
## request_mrsa                                                    23.10
## request_vre                                                      4.74
## request_cdiff                                                   11.29
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                       8.23
## as.factor(callout_month)                                        65.01
## as.factor(callout_year)                                        559.90
## as.factor(callout_dayofweek)                                    58.60
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  17.85
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   12.94
##                                                                 Pr(>Chi)
## <none>                                                                  
## cut2(oasis, g = 3)                                             0.1220234
## female                                                         0.0114725
## request_tele                                                   0.2264985
## request_mrsa                                                   1.537e-06
## request_vre                                                    0.0294004
## request_cdiff                                                  0.0007793
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     0.0835985
## as.factor(callout_month)                                       1.074e-09
## as.factor(callout_year)                                        < 2.2e-16
## as.factor(callout_dayofweek)                                   8.658e-11
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004722
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.0015491
##                                                                   
## <none>                                                            
## cut2(oasis, g = 3)                                                
## female                                                         *  
## request_tele                                                      
## request_mrsa                                                   ***
## request_vre                                                    *  
## request_cdiff                                                  ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     .  
## as.factor(callout_month)                                       ***
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                   ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_tele"
## [1] 0.2264985
## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis, 
##     g = 3) + female + request_mrsa + request_vre + request_cdiff + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") + 
##     as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 
##         1))
##                                                                Df Deviance
## <none>                                                              5526.4
## cut2(oasis, g = 3)                                              2   5530.8
## female                                                          1   5532.9
## request_mrsa                                                    1   5549.5
## request_vre                                                     1   5531.1
## request_cdiff                                                   1   5537.7
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5534.6
## as.factor(callout_month)                                       11   5591.2
## as.factor(callout_year)                                         6   6085.3
## as.factor(callout_dayofweek)                                    6   5585.0
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5544.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5539.4
##                                                                   AIC
## <none>                                                         5610.4
## cut2(oasis, g = 3)                                             5610.8
## female                                                         5614.9
## request_mrsa                                                   5631.5
## request_vre                                                    5613.1
## request_cdiff                                                  5619.7
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5610.6
## as.factor(callout_month)                                       5653.2
## as.factor(callout_year)                                        6157.3
## as.factor(callout_dayofweek)                                   5657.0
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5622.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5619.4
##                                                                   LRT
## <none>                                                               
## cut2(oasis, g = 3)                                               4.38
## female                                                           6.51
## request_mrsa                                                    23.08
## request_vre                                                      4.65
## request_cdiff                                                   11.27
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                       8.17
## as.factor(callout_month)                                        64.80
## as.factor(callout_year)                                        558.90
## as.factor(callout_dayofweek)                                    58.61
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  17.88
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   12.95
##                                                                 Pr(>Chi)
## <none>                                                                  
## cut2(oasis, g = 3)                                             0.1121047
## female                                                         0.0107553
## request_mrsa                                                   1.557e-06
## request_vre                                                    0.0310622
## request_cdiff                                                  0.0007885
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     0.0855801
## as.factor(callout_month)                                       1.173e-09
## as.factor(callout_year)                                        < 2.2e-16
## as.factor(callout_dayofweek)                                   8.628e-11
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004660
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.0015379
##                                                                   
## <none>                                                            
## cut2(oasis, g = 3)                                                
## female                                                         *  
## request_mrsa                                                   ***
## request_vre                                                    *  
## request_cdiff                                                  ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     .  
## as.factor(callout_month)                                       ***
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                   ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(oasis, g = 3)"
## [1] 0.1121047
## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ female + 
##     request_mrsa + request_vre + request_cdiff + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 
##     1):cut2(PROPFULL_BEDS, c(0.9, 1))
##                                                                Df Deviance
## <none>                                                              5530.8
## female                                                          1   5537.0
## request_mrsa                                                    1   5554.3
## request_vre                                                     1   5535.5
## request_cdiff                                                   1   5543.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5539.3
## as.factor(callout_month)                                       11   5595.4
## as.factor(callout_year)                                         6   6089.3
## as.factor(callout_dayofweek)                                    6   5588.7
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5549.0
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5544.1
##                                                                   AIC
## <none>                                                         5610.8
## female                                                         5615.0
## request_mrsa                                                   5632.3
## request_vre                                                    5613.5
## request_cdiff                                                  5621.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5611.3
## as.factor(callout_month)                                       5653.4
## as.factor(callout_year)                                        6157.3
## as.factor(callout_dayofweek)                                   5656.7
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5623.0
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5620.1
##                                                                   LRT
## <none>                                                               
## female                                                           6.23
## request_mrsa                                                    23.47
## request_vre                                                      4.69
## request_cdiff                                                   12.48
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                       8.45
## as.factor(callout_month)                                        64.60
## as.factor(callout_year)                                        558.50
## as.factor(callout_dayofweek)                                    57.84
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  18.13
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   13.30
##                                                                 Pr(>Chi)
## <none>                                                                  
## female                                                         0.0125658
## request_mrsa                                                   1.271e-06
## request_vre                                                    0.0304175
## request_cdiff                                                  0.0004111
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     0.0765210
## as.factor(callout_month)                                       1.281e-09
## as.factor(callout_year)                                        < 2.2e-16
## as.factor(callout_dayofweek)                                   1.234e-10
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004127
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.0012971
##                                                                   
## <none>                                                            
## female                                                         *  
## request_mrsa                                                   ***
## request_vre                                                    *  
## request_cdiff                                                  ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     .  
## as.factor(callout_month)                                       ***
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                   ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(los_pre_callout_days, c(1, 3, 7, 28))"
## [1] 0.076521
## Single term deletions
## 
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ female + 
##     request_mrsa + request_vre + request_cdiff + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") + 
##     as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 
##         1))
##                                                                Df Deviance
## <none>                                                              5539.3
## female                                                          1   5545.3
## request_mrsa                                                    1   5562.3
## request_vre                                                     1   5544.1
## request_cdiff                                                   1   5551.4
## as.factor(callout_month)                                       11   5602.8
## as.factor(callout_year)                                         6   6095.4
## as.factor(callout_dayofweek)                                    6   5597.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5556.4
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5553.0
##                                                                   AIC
## <none>                                                         5611.3
## female                                                         5615.3
## request_mrsa                                                   5632.3
## request_vre                                                    5614.1
## request_cdiff                                                  5621.4
## as.factor(callout_month)                                       5652.8
## as.factor(callout_year)                                        6155.4
## as.factor(callout_dayofweek)                                   5657.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5622.4
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5621.0
##                                                                   LRT
## <none>                                                               
## female                                                           6.06
## request_mrsa                                                    23.04
## request_vre                                                      4.88
## request_cdiff                                                   12.14
## as.factor(callout_month)                                        63.52
## as.factor(callout_year)                                        556.12
## as.factor(callout_dayofweek)                                    58.16
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  17.15
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   13.70
##                                                                 Pr(>Chi)
## <none>                                                                  
## female                                                         0.0138446
## request_mrsa                                                   1.588e-06
## request_vre                                                    0.0271121
## request_cdiff                                                  0.0004928
## as.factor(callout_month)                                       2.047e-09
## as.factor(callout_year)                                        < 2.2e-16
## as.factor(callout_dayofweek)                                   1.066e-10
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0006594
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.0010603
##                                                                   
## <none>                                                            
## female                                                         *  
## request_mrsa                                                   ***
## request_vre                                                    *  
## request_cdiff                                                  ***
## as.factor(callout_month)                                       ***
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                   ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_vre"
## [1] 0.0271121
## 
## Call:
## glm(formula = cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ 
##     female + request_mrsa + request_vre + request_cdiff + as.factor(callout_month) + 
##         as.factor(callout_year) + as.factor(callout_dayofweek) + 
##         as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, 
##         c(0.9, 1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)), 
##         "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, 
##         c(0.9, 1)), family = "binomial", data = d)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.4841  -0.4791  -0.3238  -0.2197   3.0144  
## 
## Coefficients:
##                                                                                Estimate
## (Intercept)                                                                    -1.73947
## female                                                                         -0.17488
## request_mrsa                                                                    0.47921
## request_vre                                                                     0.34237
## request_cdiff                                                                   0.52787
## as.factor(callout_month)2                                                      -0.01079
## as.factor(callout_month)3                                                      -0.69402
## as.factor(callout_month)4                                                      -0.39710
## as.factor(callout_month)5                                                      -0.35628
## as.factor(callout_month)6                                                      -0.47344
## as.factor(callout_month)7                                                      -0.12734
## as.factor(callout_month)8                                                       0.12819
## as.factor(callout_month)9                                                       0.24047
## as.factor(callout_month)10                                                      0.08107
## as.factor(callout_month)11                                                      0.07312
## as.factor(callout_month)12                                                     -0.54476
## as.factor(callout_year)2006                                                     0.50025
## as.factor(callout_year)2007                                                    -0.09478
## as.factor(callout_year)2008                                                    -1.54972
## as.factor(callout_year)2009                                                    -1.38900
## as.factor(callout_year)2010                                                    -1.38651
## as.factor(callout_year)2011                                                    -1.66868
## as.factor(callout_dayofweek)monday                                             -0.45487
## as.factor(callout_dayofweek)saturday                                            0.59972
## as.factor(callout_dayofweek)sunday                                              0.44095
## as.factor(callout_dayofweek)thursday                                           -0.10873
## as.factor(callout_dayofweek)tuesday                                            -0.34804
## as.factor(callout_dayofweek)wednesday                                          -0.48457
## as.factor(callout_wardid == 1)TRUE                                             -0.66724
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                     0.84106
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                     1.20867
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)   0.19134
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)   0.30378
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]   0.09684
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)  0.41306
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]  1.04129
##                                                                                Std. Error
## (Intercept)                                                                       0.28215
## female                                                                            0.07116
## request_mrsa                                                                      0.09707
## request_vre                                                                       0.15099
## request_cdiff                                                                     0.14539
## as.factor(callout_month)2                                                         0.16842
## as.factor(callout_month)3                                                         0.19323
## as.factor(callout_month)4                                                         0.18936
## as.factor(callout_month)5                                                         0.18038
## as.factor(callout_month)6                                                         0.17818
## as.factor(callout_month)7                                                         0.17706
## as.factor(callout_month)8                                                         0.16360
## as.factor(callout_month)9                                                         0.16081
## as.factor(callout_month)10                                                        0.16297
## as.factor(callout_month)11                                                        0.16509
## as.factor(callout_month)12                                                        0.18787
## as.factor(callout_year)2006                                                       0.15909
## as.factor(callout_year)2007                                                       0.16074
## as.factor(callout_year)2008                                                       0.17998
## as.factor(callout_year)2009                                                       0.18773
## as.factor(callout_year)2010                                                       0.17354
## as.factor(callout_year)2011                                                       0.18513
## as.factor(callout_dayofweek)monday                                                0.14004
## as.factor(callout_dayofweek)saturday                                              0.15988
## as.factor(callout_dayofweek)sunday                                                0.16392
## as.factor(callout_dayofweek)thursday                                              0.12697
## as.factor(callout_dayofweek)tuesday                                               0.12982
## as.factor(callout_dayofweek)wednesday                                             0.13060
## as.factor(callout_wardid == 1)TRUE                                                0.18879
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                       0.22962
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                       0.28512
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)     0.53598
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)     0.07308
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]     0.25931
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)    0.23491
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]    0.28515
##                                                                                z value
## (Intercept)                                                                     -6.165
## female                                                                          -2.458
## request_mrsa                                                                     4.937
## request_vre                                                                      2.267
## request_cdiff                                                                    3.631
## as.factor(callout_month)2                                                       -0.064
## as.factor(callout_month)3                                                       -3.592
## as.factor(callout_month)4                                                       -2.097
## as.factor(callout_month)5                                                       -1.975
## as.factor(callout_month)6                                                       -2.657
## as.factor(callout_month)7                                                       -0.719
## as.factor(callout_month)8                                                        0.784
## as.factor(callout_month)9                                                        1.495
## as.factor(callout_month)10                                                       0.497
## as.factor(callout_month)11                                                       0.443
## as.factor(callout_month)12                                                      -2.900
## as.factor(callout_year)2006                                                      3.144
## as.factor(callout_year)2007                                                     -0.590
## as.factor(callout_year)2008                                                     -8.611
## as.factor(callout_year)2009                                                     -7.399
## as.factor(callout_year)2010                                                     -7.990
## as.factor(callout_year)2011                                                     -9.013
## as.factor(callout_dayofweek)monday                                              -3.248
## as.factor(callout_dayofweek)saturday                                             3.751
## as.factor(callout_dayofweek)sunday                                               2.690
## as.factor(callout_dayofweek)thursday                                            -0.856
## as.factor(callout_dayofweek)tuesday                                             -2.681
## as.factor(callout_dayofweek)wednesday                                           -3.710
## as.factor(callout_wardid == 1)TRUE                                              -3.534
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                      3.663
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                      4.239
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)    0.357
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)    4.157
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]    0.373
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)   1.758
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]   3.652
##                                                                                Pr(>|z|)
## (Intercept)                                                                    7.05e-10
## female                                                                         0.013986
## request_mrsa                                                                   7.94e-07
## request_vre                                                                    0.023360
## request_cdiff                                                                  0.000283
## as.factor(callout_month)2                                                      0.948912
## as.factor(callout_month)3                                                      0.000329
## as.factor(callout_month)4                                                      0.035985
## as.factor(callout_month)5                                                      0.048249
## as.factor(callout_month)6                                                      0.007880
## as.factor(callout_month)7                                                      0.472029
## as.factor(callout_month)8                                                      0.433300
## as.factor(callout_month)9                                                      0.134803
## as.factor(callout_month)10                                                     0.618884
## as.factor(callout_month)11                                                     0.657844
## as.factor(callout_month)12                                                     0.003735
## as.factor(callout_year)2006                                                    0.001664
## as.factor(callout_year)2007                                                    0.555436
## as.factor(callout_year)2008                                                     < 2e-16
## as.factor(callout_year)2009                                                    1.37e-13
## as.factor(callout_year)2010                                                    1.35e-15
## as.factor(callout_year)2011                                                     < 2e-16
## as.factor(callout_dayofweek)monday                                             0.001162
## as.factor(callout_dayofweek)saturday                                           0.000176
## as.factor(callout_dayofweek)sunday                                             0.007146
## as.factor(callout_dayofweek)thursday                                           0.391775
## as.factor(callout_dayofweek)tuesday                                            0.007339
## as.factor(callout_dayofweek)wednesday                                          0.000207
## as.factor(callout_wardid == 1)TRUE                                             0.000409
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                    0.000249
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                    2.24e-05
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)  0.721092
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)  3.23e-05
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]  0.708815
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.078688
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.000260
##                                                                                   
## (Intercept)                                                                    ***
## female                                                                         *  
## request_mrsa                                                                   ***
## request_vre                                                                    *  
## request_cdiff                                                                  ***
## as.factor(callout_month)2                                                         
## as.factor(callout_month)3                                                      ***
## as.factor(callout_month)4                                                      *  
## as.factor(callout_month)5                                                      *  
## as.factor(callout_month)6                                                      ** 
## as.factor(callout_month)7                                                         
## as.factor(callout_month)8                                                         
## as.factor(callout_month)9                                                         
## as.factor(callout_month)10                                                        
## as.factor(callout_month)11                                                        
## as.factor(callout_month)12                                                     ** 
## as.factor(callout_year)2006                                                    ** 
## as.factor(callout_year)2007                                                       
## as.factor(callout_year)2008                                                    ***
## as.factor(callout_year)2009                                                    ***
## as.factor(callout_year)2010                                                    ***
## as.factor(callout_year)2011                                                    ***
## as.factor(callout_dayofweek)monday                                             ** 
## as.factor(callout_dayofweek)saturday                                           ***
## as.factor(callout_dayofweek)sunday                                             ** 
## as.factor(callout_dayofweek)thursday                                              
## as.factor(callout_dayofweek)tuesday                                            ** 
## as.factor(callout_dayofweek)wednesday                                          ***
## as.factor(callout_wardid == 1)TRUE                                             ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                    ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                    ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)     
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)  ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]     
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) .  
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6478.9  on 9672  degrees of freedom
## Residual deviance: 5539.3  on 9637  degrees of freedom
## AIC: 5611.3
## 
## Number of Fisher Scoring iterations: 6
    cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]”
    Odds Ratio CI p
(Intercept)   0.18 0.10 – 0.30 <.001
female   0.84 0.73 – 0.97 .014
request_mrsa   1.61 1.33 – 1.95 <.001
request_vre   1.41 1.04 – 1.88 .023
request_cdiff   1.70 1.27 – 2.24 <.001
as.factor(callout_month)
as.factor(callout_month)2   0.99 0.71 – 1.38 .949
as.factor(callout_month)3   0.50 0.34 – 0.73 <.001
as.factor(callout_month)4   0.67 0.46 – 0.97 .036
as.factor(callout_month)5   0.70 0.49 – 1.00 .048
as.factor(callout_month)6   0.62 0.44 – 0.88 .008
as.factor(callout_month)7   0.88 0.62 – 1.24 .472
as.factor(callout_month)8   1.14 0.83 – 1.57 .433
as.factor(callout_month)9   1.27 0.93 – 1.75 .135
as.factor(callout_month)10   1.08 0.79 – 1.49 .619
as.factor(callout_month)11   1.08 0.78 – 1.49 .658
as.factor(callout_month)12   0.58 0.40 – 0.84 .004
as.factor(callout_year)
as.factor(callout_year)2006   1.65 1.21 – 2.26 .002
as.factor(callout_year)2007   0.91 0.67 – 1.25 .555
as.factor(callout_year)2008   0.21 0.15 – 0.30 <.001
as.factor(callout_year)2009   0.25 0.17 – 0.36 <.001
as.factor(callout_year)2010   0.25 0.18 – 0.35 <.001
as.factor(callout_year)2011   0.19 0.13 – 0.27 <.001
as.factor(callout_dayofweek)
as.factor(callout_dayofweek)monday   0.63 0.48 – 0.83 .001
as.factor(callout_dayofweek)saturday   1.82 1.33 – 2.49 <.001
as.factor(callout_dayofweek)sunday   1.55 1.13 – 2.14 .007
as.factor(callout_dayofweek)thursday   0.90 0.70 – 1.15 .392
as.factor(callout_dayofweek)tuesday   0.71 0.55 – 0.91 .007
as.factor(callout_dayofweek)wednesday   0.62 0.48 – 0.80 <.001
as.factor(callout_wardid == 1)TRUE   0.51 0.36 – 0.75 <.001
cut2(PROPFULL_BEDS, c(0.9, 1))
cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)   2.32 1.49 – 3.66 <.001
cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]   3.35 1.91 – 5.85 <.001
relevel(cut2(hourofcallout2, c(7, 12, 19)), “[ 7.000,12.000)”)
relevel(cut2(hourofcallout2, c(7, 12, 19)), “[ 7.000,12.000)”)[ 0.117, 7.000)   1.21 0.36 – 3.08 .721
relevel(cut2(hourofcallout2, c(7, 12, 19)), “[ 7.000,12.000)”)[12.000,19.000)   1.35 1.17 – 1.56 <.001
relevel(cut2(hourofcallout2, c(7, 12, 19)), “[ 7.000,12.000)”)[19.000,23.867]   1.10 0.64 – 1.79 .709
as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)   1.51 0.95 – 2.39 .079
as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]   2.83 1.62 – 4.98 <.001
Observations   9673

Answer 1:

Solid evidence: When Hospital is near or over capacity, when mrsa/cdiff have to be taken into account (more likely); When the callout is made during rounds or in more recent calendar year (less likely).

Less Solid Evidence: All of the above PLUS: On certain days of the week, sicker patients (OASIS), certain months, vre (more likely); Female (less likely). Effect modified of “first available bed” and propfull beds. E.g., when hospital is <90% used: “first available bed” => less likely to have a long delay, but effect is negated or reversed when hospitals are full.

Question 2: Do people who have long discharge delays (>24 hours) die more often?

We will build a model in a similar way as before, but add the long discharge delay (>24hrs) into a model for hospital mortality, retaining our exposure of interested (DD) throughout.

Patient Characteristics Overall
variable_name level 0 1 p test
n 9140 533
micu (%) 1 9140 (100.0) 533 (100.0) NA
age (mean (sd)) 63.36 (18.02) 69.16 (15.63) <0.001
callout_month (%) 1 734 ( 8.0) 40 ( 7.5) 0.485
2 699 ( 7.6) 39 ( 7.3)
3 680 ( 7.4) 53 ( 9.9)
4 681 ( 7.5) 45 ( 8.4)
5 751 ( 8.2) 43 ( 8.1)
6 705 ( 7.7) 40 ( 7.5)
7 737 ( 8.1) 42 ( 7.9)
8 820 ( 9.0) 57 ( 10.7)
9 808 ( 8.8) 43 ( 8.1)
10 870 ( 9.5) 44 ( 8.3)
11 788 ( 8.6) 35 ( 6.6)
12 867 ( 9.5) 52 ( 9.8)
female (%) 0 4662 ( 51.0) 294 ( 55.2) 0.069
1 4478 ( 49.0) 239 ( 44.8)
request_tele (%) 0 5943 ( 65.0) 375 ( 70.4) 0.014
1 3197 ( 35.0) 158 ( 29.6)
request_resp (%) 0 8987 ( 98.3) 527 ( 98.9) 0.428
1 153 ( 1.7) 6 ( 1.1)
request_cdiff (%) 0 8709 ( 95.3) 481 ( 90.2) <0.001
1 431 ( 4.7) 52 ( 9.8)
request_mrsa (%) 0 7948 ( 87.0) 457 ( 85.7) 0.457
1 1192 ( 13.0) 76 ( 14.3)
request_vre (%) 0 8704 ( 95.2) 478 ( 89.7) <0.001
1 436 ( 4.8) 55 ( 10.3)
oasis (mean (sd)) 29.55 (7.14) 34.79 (8.12) <0.001
elixhauser_hospital (mean (sd)) 3.26 (7.20) 8.38 (6.93) <0.001
ethnicity (%) White 6515 ( 71.3) 395 ( 74.1) 0.008
African American/Black 1436 ( 15.7) 58 ( 10.9)
Other 1189 ( 13.0) 80 ( 15.0)
MED_SERVICE (%) FALSE 659 ( 7.2) 33 ( 6.2) 0.423
TRUE 8481 ( 92.8) 500 ( 93.8)
HOSP_FREE_DAYS (median [IQR]) 24.24 [21.23, 26.06] 0.00 [0.00, 0.00] <0.001 nonnorm
callout_dayofweek (%) friday 1368 ( 15.0) 74 ( 13.9) 0.249
monday 1227 ( 13.4) 73 ( 13.7)
saturday 1229 ( 13.4) 63 ( 11.8)
sunday 1202 ( 13.2) 65 ( 12.2)
thursday 1326 ( 14.5) 86 ( 16.1)
tuesday 1326 ( 14.5) 96 ( 18.0)
wednesday 1462 ( 16.0) 76 ( 14.3)
CALLOUT_DURING_NIGHT (%) FALSE 9081 ( 99.4) 526 ( 98.7) 0.121
TRUE 59 ( 0.6) 7 ( 1.3)
CALLOUT_DURING_ROUNDS (%) FALSE 3583 ( 39.2) 233 ( 43.7) 0.043
TRUE 5557 ( 60.8) 300 ( 56.3)
DISCHARGEDELAY_HOURS (mean (sd)) 10.31 (10.16) 11.47 (12.12) 0.011
hourofcallout2 (median [IQR]) 11.38 [10.10, 13.15] 11.60 [10.23, 13.52] 0.076 nonnorm
PROPFULL_BEDS (mean (sd)) 0.91 (0.09) 0.91 (0.09) 0.543
postcalldaycat2 (%) 0 7393 ( 80.9) 407 ( 76.4) 0.012
[1,5] 1747 ( 19.1) 126 ( 23.6)
los_preicu_days (median [IQR]) 0.00 [0.00, 0.13] 0.00 [0.00, 1.87] <0.001 nonnorm
los_post_callout_days (median [IQR]) 4.13 [2.27, 7.12] 6.86 [2.87, 14.67] <0.001 nonnorm
los_post_icu_days (median [IQR]) 3.76 [1.94, 6.77] 6.36 [2.59, 14.16] <0.001 nonnorm
los_pre_callout_days (median [IQR]) 1.74 [0.93, 3.55] 3.48 [1.51, 7.67] <0.001 nonnorm
callout_year (%) 2005 346 ( 3.8) 30 ( 5.6) 0.015
2006 996 ( 10.9) 74 ( 13.9)
2007 1329 ( 14.5) 88 ( 16.5)
2008 1561 ( 17.1) 77 ( 14.4)
2009 1586 ( 17.4) 82 ( 15.4)
2010 1639 ( 17.9) 100 ( 18.8)
2011 1683 ( 18.4) 82 ( 15.4)
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele + 
##     request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              3548.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3548.4
## cut2(oasis, g = 3)                                              2   3661.3
## cut2(age, g = 3)                                                2   3550.5
## female                                                          1   3551.4
## request_tele                                                    1   3558.1
## request_resp                                                    1   3548.4
## request_mrsa                                                    1   3548.7
## request_vre                                                     1   3562.7
## request_cdiff                                                   1   3554.0
## cut2(elixhauser_hospital, g = 3)                                2   3648.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3649.1
## as.factor(callout_month)                                       11   3563.6
## as.factor(callout_year)                                         6   3566.8
## as.factor(callout_dayofweek)                                    6   3555.3
## MED_SERVICE                                                     1   3551.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3554.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   3550.5
##                                                                   AIC
## <none>                                                         3648.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3646.4
## cut2(oasis, g = 3)                                             3757.3
## cut2(age, g = 3)                                               3646.5
## female                                                         3649.4
## request_tele                                                   3656.1
## request_resp                                                   3646.4
## request_mrsa                                                   3646.7
## request_vre                                                    3660.7
## request_cdiff                                                  3652.0
## cut2(elixhauser_hospital, g = 3)                               3744.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3741.1
## as.factor(callout_month)                                       3641.6
## as.factor(callout_year)                                        3654.8
## as.factor(callout_dayofweek)                                   3643.3
## MED_SERVICE                                                    3649.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3648.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  3646.5
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.000
## cut2(oasis, g = 3)                                             112.908
## cut2(age, g = 3)                                                 2.067
## female                                                           3.022
## request_tele                                                     9.682
## request_resp                                                     0.052
## request_mrsa                                                     0.285
## request_vre                                                     14.309
## request_cdiff                                                    5.582
## cut2(elixhauser_hospital, g = 3)                               100.439
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     100.720
## as.factor(callout_month)                                        15.184
## as.factor(callout_year)                                         18.444
## as.factor(callout_dayofweek)                                     6.953
## MED_SERVICE                                                      2.846
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.720
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    2.078
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.9868110
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.3557399
## female                                                         0.0821435
## request_tele                                                   0.0018605
## request_resp                                                   0.8204068
## request_mrsa                                                   0.5931973
## request_vre                                                    0.0001551
## request_cdiff                                                  0.0181414
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.1742241
## as.factor(callout_year)                                        0.0052129
## as.factor(callout_dayofweek)                                   0.3251854
## MED_SERVICE                                                    0.0915849
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1260420
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.3538647
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                                  
## female                                                         .  
## request_tele                                                   ** 
## request_resp                                                      
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  *  
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ** 
## as.factor(callout_dayofweek)                                      
## MED_SERVICE                                                    .  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + request_tele + request_vre + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_wardid == 
##     1)
##                                                             Df Deviance
## <none>                                                           3607.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1   3608.5
## cut2(oasis, g = 3)                                           2   3746.4
## request_tele                                                 1   3618.3
## request_vre                                                  1   3620.3
## cut2(elixhauser_hospital, g = 3)                             2   3727.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4   3706.6
## as.factor(callout_wardid == 1)                               1   3636.2
##                                                                AIC     LRT
## <none>                                                      3633.7        
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3632.5   0.878
## cut2(oasis, g = 3)                                          3768.4 138.690
## request_tele                                                3642.3  10.614
## request_vre                                                 3644.3  12.661
## cut2(elixhauser_hospital, g = 3)                            3749.4 119.730
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  3724.6  98.922
## as.factor(callout_wardid == 1)                              3660.2  28.563
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.3487705    
## cut2(oasis, g = 3)                                          < 2.2e-16 ***
## request_tele                                                0.0011223 ** 
## request_vre                                                 0.0003733 ***
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_wardid == 1)                              9.071e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + female + request_tele + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid == 
##     1) + MED_SERVICE
##                                                             Df Deviance
## <none>                                                           3581.1
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1   3581.1
## cut2(oasis, g = 3)                                           2   3715.3
## female                                                       1   3584.0
## request_tele                                                 1   3590.2
## request_vre                                                  1   3594.3
## request_cdiff                                                1   3587.5
## cut2(elixhauser_hospital, g = 3)                             2   3689.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4   3679.1
## as.factor(callout_year)                                      6   3596.6
## as.factor(callout_wardid == 1)                               1   3616.7
## MED_SERVICE                                                  1   3583.6
##                                                                AIC     LRT
## <none>                                                      3625.1        
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3623.1   0.021
## cut2(oasis, g = 3)                                          3755.3 134.221
## female                                                      3626.0   2.899
## request_tele                                                3632.2   9.117
## request_vre                                                 3636.3  13.194
## request_cdiff                                               3629.5   6.433
## cut2(elixhauser_hospital, g = 3)                            3729.6 108.553
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  3715.1  98.067
## as.factor(callout_year)                                     3628.6  15.491
## as.factor(callout_wardid == 1)                              3658.7  35.666
## MED_SERVICE                                                 3625.6   2.520
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8851019    
## cut2(oasis, g = 3)                                          < 2.2e-16 ***
## female                                                      0.0886075 .  
## request_tele                                                0.0025331 ** 
## request_vre                                                 0.0002808 ***
## request_cdiff                                               0.0112050 *  
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_year)                                     0.0167636 *  
## as.factor(callout_wardid == 1)                              2.342e-09 ***
## MED_SERVICE                                                 0.1123962    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## glm(formula = hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)"), family = "binomial", data = d)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.6299  -0.3551  -0.2382  -0.1573   3.2778  
## 
## Coefficients:
##                                                                                 Estimate
## (Intercept)                                                                    -4.082023
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                 0.002475
## cut2(oasis, g = 3)[27,34)                                                       0.667113
## cut2(oasis, g = 3)[34,64]                                                       1.381300
## cut2(age, g = 3)[56.1,73.8)                                                     0.068712
## cut2(age, g = 3)[73.8,91.4]                                                     0.178809
## female                                                                         -0.164041
## request_tele                                                                   -0.314127
## request_resp                                                                   -0.096674
## request_mrsa                                                                   -0.072664
## request_vre                                                                     0.660880
## request_cdiff                                                                   0.403258
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                        0.717941
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        1.274143
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                    -0.070743
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                     0.523098
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                     0.885316
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                     2.440485
## as.factor(callout_month)2                                                       0.043364
## as.factor(callout_month)3                                                       0.292223
## as.factor(callout_month)4                                                       0.167589
## as.factor(callout_month)5                                                       0.041533
## as.factor(callout_month)6                                                       0.045715
## as.factor(callout_month)7                                                       0.050874
## as.factor(callout_month)8                                                       0.257792
## as.factor(callout_month)9                                                      -0.139124
## as.factor(callout_month)10                                                     -0.176043
## as.factor(callout_month)11                                                     -0.417376
## as.factor(callout_month)12                                                      0.041012
## as.factor(callout_year)2006                                                    -0.376682
## as.factor(callout_year)2007                                                    -0.511288
## as.factor(callout_year)2008                                                    -0.732877
## as.factor(callout_year)2009                                                    -0.859038
## as.factor(callout_year)2010                                                    -0.599114
## as.factor(callout_year)2011                                                    -0.815269
## as.factor(callout_dayofweek)monday                                              0.036628
## as.factor(callout_dayofweek)saturday                                            0.040050
## as.factor(callout_dayofweek)sunday                                              0.036155
## as.factor(callout_dayofweek)thursday                                            0.153686
## as.factor(callout_dayofweek)tuesday                                             0.320468
## as.factor(callout_dayofweek)wednesday                                          -0.077525
## as.factor(callout_wardid == 1)TRUE                                             -0.530349
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                     0.237551
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                     0.204866
## MED_SERVICETRUE                                                                 0.336763
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)   0.905215
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)   0.092518
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]   0.422993
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -0.348663
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.284574
##                                                                                Std. Error
## (Intercept)                                                                      0.436454
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                  0.149667
## cut2(oasis, g = 3)[27,34)                                                        0.145238
## cut2(oasis, g = 3)[34,64]                                                        0.142961
## cut2(age, g = 3)[56.1,73.8)                                                      0.128484
## cut2(age, g = 3)[73.8,91.4]                                                      0.129804
## female                                                                           0.094528
## request_tele                                                                     0.102492
## request_resp                                                                     0.431362
## request_mrsa                                                                     0.136850
## request_vre                                                                      0.165746
## request_cdiff                                                                    0.164761
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                         0.151081
## cut2(elixhauser_hospital, g = 3)[  7,31]                                         0.140125
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                      0.136524
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                      0.141507
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                      0.148620
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                      0.328256
## as.factor(callout_month)2                                                        0.241413
## as.factor(callout_month)3                                                        0.227627
## as.factor(callout_month)4                                                        0.235199
## as.factor(callout_month)5                                                        0.236091
## as.factor(callout_month)6                                                        0.240511
## as.factor(callout_month)7                                                        0.237562
## as.factor(callout_month)8                                                        0.224792
## as.factor(callout_month)9                                                        0.237640
## as.factor(callout_month)10                                                       0.237104
## as.factor(callout_month)11                                                       0.249786
## as.factor(callout_month)12                                                       0.230016
## as.factor(callout_year)2006                                                      0.244897
## as.factor(callout_year)2007                                                      0.241150
## as.factor(callout_year)2008                                                      0.245039
## as.factor(callout_year)2009                                                      0.246656
## as.factor(callout_year)2010                                                      0.240234
## as.factor(callout_year)2011                                                      0.243189
## as.factor(callout_dayofweek)monday                                               0.178801
## as.factor(callout_dayofweek)saturday                                             0.196377
## as.factor(callout_dayofweek)sunday                                               0.194762
## as.factor(callout_dayofweek)thursday                                             0.176102
## as.factor(callout_dayofweek)tuesday                                              0.171798
## as.factor(callout_dayofweek)wednesday                                            0.182603
## as.factor(callout_wardid == 1)TRUE                                               0.192263
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                      0.233908
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                      0.311475
## MED_SERVICETRUE                                                                  0.206249
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)    0.445336
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)    0.097977
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]    0.289739
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)   0.248295
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]   0.329251
##                                                                                z value
## (Intercept)                                                                     -9.353
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                  0.017
## cut2(oasis, g = 3)[27,34)                                                        4.593
## cut2(oasis, g = 3)[34,64]                                                        9.662
## cut2(age, g = 3)[56.1,73.8)                                                      0.535
## cut2(age, g = 3)[73.8,91.4]                                                      1.378
## female                                                                          -1.735
## request_tele                                                                    -3.065
## request_resp                                                                    -0.224
## request_mrsa                                                                    -0.531
## request_vre                                                                      3.987
## request_cdiff                                                                    2.448
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                         4.752
## cut2(elixhauser_hospital, g = 3)[  7,31]                                         9.093
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                     -0.518
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                      3.697
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                      5.957
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                      7.435
## as.factor(callout_month)2                                                        0.180
## as.factor(callout_month)3                                                        1.284
## as.factor(callout_month)4                                                        0.713
## as.factor(callout_month)5                                                        0.176
## as.factor(callout_month)6                                                        0.190
## as.factor(callout_month)7                                                        0.214
## as.factor(callout_month)8                                                        1.147
## as.factor(callout_month)9                                                       -0.585
## as.factor(callout_month)10                                                      -0.742
## as.factor(callout_month)11                                                      -1.671
## as.factor(callout_month)12                                                       0.178
## as.factor(callout_year)2006                                                     -1.538
## as.factor(callout_year)2007                                                     -2.120
## as.factor(callout_year)2008                                                     -2.991
## as.factor(callout_year)2009                                                     -3.483
## as.factor(callout_year)2010                                                     -2.494
## as.factor(callout_year)2011                                                     -3.352
## as.factor(callout_dayofweek)monday                                               0.205
## as.factor(callout_dayofweek)saturday                                             0.204
## as.factor(callout_dayofweek)sunday                                               0.186
## as.factor(callout_dayofweek)thursday                                             0.873
## as.factor(callout_dayofweek)tuesday                                              1.865
## as.factor(callout_dayofweek)wednesday                                           -0.425
## as.factor(callout_wardid == 1)TRUE                                              -2.758
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                      1.016
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                      0.658
## MED_SERVICETRUE                                                                  1.633
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)    2.033
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)    0.944
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]    1.460
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)  -1.404
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]  -0.864
##                                                                                Pr(>|z|)
## (Intercept)                                                                     < 2e-16
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                0.986808
## cut2(oasis, g = 3)[27,34)                                                      4.36e-06
## cut2(oasis, g = 3)[34,64]                                                       < 2e-16
## cut2(age, g = 3)[56.1,73.8)                                                    0.592798
## cut2(age, g = 3)[73.8,91.4]                                                    0.168347
## female                                                                         0.082676
## request_tele                                                                   0.002177
## request_resp                                                                   0.822668
## request_mrsa                                                                   0.595435
## request_vre                                                                    6.68e-05
## request_cdiff                                                                  0.014384
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                       2.01e-06
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                    0.604340
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                    0.000218
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                    2.57e-09
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                    1.05e-13
## as.factor(callout_month)2                                                      0.857448
## as.factor(callout_month)3                                                      0.199218
## as.factor(callout_month)4                                                      0.476128
## as.factor(callout_month)5                                                      0.860357
## as.factor(callout_month)6                                                      0.849251
## as.factor(callout_month)7                                                      0.830431
## as.factor(callout_month)8                                                      0.251463
## as.factor(callout_month)9                                                      0.558252
## as.factor(callout_month)10                                                     0.457800
## as.factor(callout_month)11                                                     0.094734
## as.factor(callout_month)12                                                     0.858487
## as.factor(callout_year)2006                                                    0.124018
## as.factor(callout_year)2007                                                    0.033989
## as.factor(callout_year)2008                                                    0.002782
## as.factor(callout_year)2009                                                    0.000496
## as.factor(callout_year)2010                                                    0.012636
## as.factor(callout_year)2011                                                    0.000801
## as.factor(callout_dayofweek)monday                                             0.837686
## as.factor(callout_dayofweek)saturday                                           0.838397
## as.factor(callout_dayofweek)sunday                                             0.852731
## as.factor(callout_dayofweek)thursday                                           0.382820
## as.factor(callout_dayofweek)tuesday                                            0.062128
## as.factor(callout_dayofweek)wednesday                                          0.671161
## as.factor(callout_wardid == 1)TRUE                                             0.005808
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                    0.309834
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                    0.510713
## MED_SERVICETRUE                                                                0.102512
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)  0.042087
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)  0.345028
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]  0.144314
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.160251
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.387420
##                                                                                   
## (Intercept)                                                                    ***
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                   
## cut2(oasis, g = 3)[27,34)                                                      ***
## cut2(oasis, g = 3)[34,64]                                                      ***
## cut2(age, g = 3)[56.1,73.8)                                                       
## cut2(age, g = 3)[73.8,91.4]                                                       
## female                                                                         .  
## request_tele                                                                   ** 
## request_resp                                                                      
## request_mrsa                                                                      
## request_vre                                                                    ***
## request_cdiff                                                                  *  
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                       ***
## cut2(elixhauser_hospital, g = 3)[  7,31]                                       ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                       
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                    ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                    ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                    ***
## as.factor(callout_month)2                                                         
## as.factor(callout_month)3                                                         
## as.factor(callout_month)4                                                         
## as.factor(callout_month)5                                                         
## as.factor(callout_month)6                                                         
## as.factor(callout_month)7                                                         
## as.factor(callout_month)8                                                         
## as.factor(callout_month)9                                                         
## as.factor(callout_month)10                                                        
## as.factor(callout_month)11                                                     .  
## as.factor(callout_month)12                                                        
## as.factor(callout_year)2006                                                       
## as.factor(callout_year)2007                                                    *  
## as.factor(callout_year)2008                                                    ** 
## as.factor(callout_year)2009                                                    ***
## as.factor(callout_year)2010                                                    *  
## as.factor(callout_year)2011                                                    ***
## as.factor(callout_dayofweek)monday                                                
## as.factor(callout_dayofweek)saturday                                              
## as.factor(callout_dayofweek)sunday                                                
## as.factor(callout_dayofweek)thursday                                              
## as.factor(callout_dayofweek)tuesday                                            .  
## as.factor(callout_dayofweek)wednesday                                             
## as.factor(callout_wardid == 1)TRUE                                             ** 
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                       
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                       
## MED_SERVICETRUE                                                                   
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)  *  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)     
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]     
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)    
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 4126.0  on 9672  degrees of freedom
## Residual deviance: 3548.4  on 9623  degrees of freedom
## AIC: 3648.4
## 
## Number of Fisher Scoring iterations: 6
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele + 
##     request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              3548.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3548.4
## cut2(oasis, g = 3)                                              2   3661.3
## cut2(age, g = 3)                                                2   3550.5
## female                                                          1   3551.4
## request_tele                                                    1   3558.1
## request_resp                                                    1   3548.4
## request_mrsa                                                    1   3548.7
## request_vre                                                     1   3562.7
## request_cdiff                                                   1   3554.0
## cut2(elixhauser_hospital, g = 3)                                2   3648.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3649.1
## as.factor(callout_month)                                       11   3563.6
## as.factor(callout_year)                                         6   3566.8
## as.factor(callout_dayofweek)                                    6   3555.3
## MED_SERVICE                                                     1   3551.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3554.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   3550.5
##                                                                   AIC
## <none>                                                         3648.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3646.4
## cut2(oasis, g = 3)                                             3757.3
## cut2(age, g = 3)                                               3646.5
## female                                                         3649.4
## request_tele                                                   3656.1
## request_resp                                                   3646.4
## request_mrsa                                                   3646.7
## request_vre                                                    3660.7
## request_cdiff                                                  3652.0
## cut2(elixhauser_hospital, g = 3)                               3744.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3741.1
## as.factor(callout_month)                                       3641.6
## as.factor(callout_year)                                        3654.8
## as.factor(callout_dayofweek)                                   3643.3
## MED_SERVICE                                                    3649.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3648.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  3646.5
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele + 
##     request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              3548.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3548.4
## cut2(oasis, g = 3)                                              2   3661.3
## cut2(age, g = 3)                                                2   3550.5
## female                                                          1   3551.4
## request_tele                                                    1   3558.1
## request_resp                                                    1   3548.4
## request_mrsa                                                    1   3548.7
## request_vre                                                     1   3562.7
## request_cdiff                                                   1   3554.0
## cut2(elixhauser_hospital, g = 3)                                2   3648.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3649.1
## as.factor(callout_month)                                       11   3563.6
## as.factor(callout_year)                                         6   3566.8
## as.factor(callout_dayofweek)                                    6   3555.3
## MED_SERVICE                                                     1   3551.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3554.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   3550.5
##                                                                   AIC
## <none>                                                         3648.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3646.4
## cut2(oasis, g = 3)                                             3757.3
## cut2(age, g = 3)                                               3646.5
## female                                                         3649.4
## request_tele                                                   3656.1
## request_resp                                                   3646.4
## request_mrsa                                                   3646.7
## request_vre                                                    3660.7
## request_cdiff                                                  3652.0
## cut2(elixhauser_hospital, g = 3)                               3744.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3741.1
## as.factor(callout_month)                                       3641.6
## as.factor(callout_year)                                        3654.8
## as.factor(callout_dayofweek)                                   3643.3
## MED_SERVICE                                                    3649.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3648.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  3646.5
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.000
## cut2(oasis, g = 3)                                             112.908
## cut2(age, g = 3)                                                 2.067
## female                                                           3.022
## request_tele                                                     9.682
## request_resp                                                     0.052
## request_mrsa                                                     0.285
## request_vre                                                     14.309
## request_cdiff                                                    5.582
## cut2(elixhauser_hospital, g = 3)                               100.439
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     100.720
## as.factor(callout_month)                                        15.184
## as.factor(callout_year)                                         18.444
## as.factor(callout_dayofweek)                                     6.953
## MED_SERVICE                                                      2.846
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.720
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    2.078
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.9868110
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.3557399
## female                                                         0.0821435
## request_tele                                                   0.0018605
## request_resp                                                   0.8204068
## request_mrsa                                                   0.5931973
## request_vre                                                    0.0001551
## request_cdiff                                                  0.0181414
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.1742241
## as.factor(callout_year)                                        0.0052129
## as.factor(callout_dayofweek)                                   0.3251854
## MED_SERVICE                                                    0.0915849
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1260420
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.3538647
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                                  
## female                                                         .  
## request_tele                                                   ** 
## request_resp                                                      
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  *  
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ** 
## as.factor(callout_dayofweek)                                      
## MED_SERVICE                                                    .  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_resp"
## [1] 0.8204068
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele + 
##     request_mrsa + request_vre + request_cdiff + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, 
##     c(0.9, 1))
##                                                                Df Deviance
## <none>                                                              3548.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3548.4
## cut2(oasis, g = 3)                                              2   3661.4
## cut2(age, g = 3)                                                2   3550.5
## female                                                          1   3551.5
## request_tele                                                    1   3558.1
## request_mrsa                                                    1   3548.7
## request_vre                                                     1   3562.8
## request_cdiff                                                   1   3554.0
## cut2(elixhauser_hospital, g = 3)                                2   3648.9
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3649.4
## as.factor(callout_month)                                       11   3563.6
## as.factor(callout_year)                                         6   3567.0
## as.factor(callout_dayofweek)                                    6   3555.4
## MED_SERVICE                                                     1   3551.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3554.2
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   3550.5
##                                                                   AIC
## <none>                                                         3646.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3644.4
## cut2(oasis, g = 3)                                             3755.4
## cut2(age, g = 3)                                               3644.5
## female                                                         3647.5
## request_tele                                                   3654.1
## request_mrsa                                                   3644.7
## request_vre                                                    3658.8
## request_cdiff                                                  3650.0
## cut2(elixhauser_hospital, g = 3)                               3742.9
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3739.4
## as.factor(callout_month)                                       3639.6
## as.factor(callout_year)                                        3653.0
## as.factor(callout_dayofweek)                                   3641.4
## MED_SERVICE                                                    3647.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3646.2
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  3644.5
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.000
## cut2(oasis, g = 3)                                             112.917
## cut2(age, g = 3)                                                 2.086
## female                                                           3.013
## request_tele                                                     9.675
## request_mrsa                                                     0.290
## request_vre                                                     14.327
## request_cdiff                                                    5.575
## cut2(elixhauser_hospital, g = 3)                               100.433
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     100.984
## as.factor(callout_month)                                        15.191
## as.factor(callout_year)                                         18.529
## as.factor(callout_dayofweek)                                     6.961
## MED_SERVICE                                                      2.843
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.750
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    2.099
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.9851924
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.3524507
## female                                                         0.0826076
## request_tele                                                   0.0018683
## request_mrsa                                                   0.5902019
## request_vre                                                    0.0001536
## request_cdiff                                                  0.0182146
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.1739024
## as.factor(callout_year)                                        0.0050380
## as.factor(callout_dayofweek)                                   0.3244406
## MED_SERVICE                                                    0.0917853
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1244301
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.3501837
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                                  
## female                                                         .  
## request_tele                                                   ** 
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  *  
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ** 
## as.factor(callout_dayofweek)                                      
## MED_SERVICE                                                    .  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_mrsa"
## [1] 0.5902019
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, 
##     c(0.9, 1))
##                                                                Df Deviance
## <none>                                                              3548.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3548.7
## cut2(oasis, g = 3)                                              2   3661.5
## cut2(age, g = 3)                                                2   3550.8
## female                                                          1   3551.7
## request_tele                                                    1   3558.4
## request_vre                                                     1   3562.8
## request_cdiff                                                   1   3554.4
## cut2(elixhauser_hospital, g = 3)                                2   3649.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3649.5
## as.factor(callout_month)                                       11   3564.0
## as.factor(callout_year)                                         6   3567.4
## as.factor(callout_dayofweek)                                    6   3555.7
## MED_SERVICE                                                     1   3551.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3554.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   3550.8
##                                                                   AIC
## <none>                                                         3644.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3642.7
## cut2(oasis, g = 3)                                             3753.5
## cut2(age, g = 3)                                               3642.8
## female                                                         3645.7
## request_tele                                                   3652.4
## request_vre                                                    3656.8
## request_cdiff                                                  3648.4
## cut2(elixhauser_hospital, g = 3)                               3741.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3737.5
## as.factor(callout_month)                                       3638.0
## as.factor(callout_year)                                        3651.4
## as.factor(callout_dayofweek)                                   3639.7
## MED_SERVICE                                                    3645.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3644.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  3642.8
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.000
## cut2(oasis, g = 3)                                             112.800
## cut2(age, g = 3)                                                 2.037
## female                                                           3.007
## request_tele                                                     9.698
## request_vre                                                     14.040
## request_cdiff                                                    5.635
## cut2(elixhauser_hospital, g = 3)                               100.599
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     100.739
## as.factor(callout_month)                                        15.249
## as.factor(callout_year)                                         18.693
## as.factor(callout_dayofweek)                                     6.925
## MED_SERVICE                                                      2.775
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.782
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    2.090
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.9945113
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.3611395
## female                                                         0.0829036
## request_tele                                                   0.0018452
## request_vre                                                    0.0001789
## request_cdiff                                                  0.0176010
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.1713729
## as.factor(callout_year)                                        0.0047149
## as.factor(callout_dayofweek)                                   0.3278483
## MED_SERVICE                                                    0.0957744
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1227164
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.3517602
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                                  
## female                                                         .  
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  *  
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ** 
## as.factor(callout_dayofweek)                                      
## MED_SERVICE                                                    .  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(age, g = 3)"
## [1] 0.3611395
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              3550.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3550.8
## cut2(oasis, g = 3)                                              2   3663.5
## cut2(age, g = 3)                                                2   3552.8
## female                                                          1   3553.7
## request_tele                                                    1   3560.6
## request_vre                                                     1   3564.8
## request_cdiff                                                   1   3556.7
## cut2(elixhauser_hospital, g = 3)                                2   3652.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3650.8
## as.factor(callout_month)                                       11   3565.9
## as.factor(callout_year)                                         6   3569.6
## as.factor(callout_dayofweek)                                    6   3557.7
## as.factor(callout_wardid == 1)                                  1   3587.5
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  2   3550.9
## MED_SERVICE                                                     1   3553.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3556.3
##                                                                   AIC
## <none>                                                         3642.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3640.8
## cut2(oasis, g = 3)                                             3751.5
## cut2(age, g = 3)                                               3640.8
## female                                                         3643.7
## request_tele                                                   3650.6
## request_vre                                                    3654.8
## request_cdiff                                                  3646.7
## cut2(elixhauser_hospital, g = 3)                               3740.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3734.8
## as.factor(callout_month)                                       3635.9
## as.factor(callout_year)                                        3649.6
## as.factor(callout_dayofweek)                                   3637.7
## as.factor(callout_wardid == 1)                                 3677.5
## cut2(PROPFULL_BEDS, c(0.9, 1))                                 3638.9
## MED_SERVICE                                                    3643.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3642.3
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.001
## cut2(oasis, g = 3)                                             112.726
## cut2(age, g = 3)                                                 1.964
## female                                                           2.890
## request_tele                                                     9.764
## request_vre                                                     14.009
## request_cdiff                                                    5.898
## cut2(elixhauser_hospital, g = 3)                               101.574
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      99.934
## as.factor(callout_month)                                        15.079
## as.factor(callout_year)                                         18.775
## as.factor(callout_dayofweek)                                     6.905
## as.factor(callout_wardid == 1)                                  36.655
## cut2(PROPFULL_BEDS, c(0.9, 1))                                   0.048
## MED_SERVICE                                                      2.680
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.461
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.9736030
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.3746428
## female                                                         0.0891327
## request_tele                                                   0.0017800
## request_vre                                                    0.0001819
## request_cdiff                                                  0.0151560
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.1788982
## as.factor(callout_year)                                        0.0045604
## as.factor(callout_dayofweek)                                   0.3297315
## as.factor(callout_wardid == 1)                                  1.41e-09
## cut2(PROPFULL_BEDS, c(0.9, 1))                                 0.9761058
## MED_SERVICE                                                    0.1016153
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1410098
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                                  
## female                                                         .  
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  *  
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ** 
## as.factor(callout_dayofweek)                                      
## as.factor(callout_wardid == 1)                                 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                                    
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(PROPFULL_BEDS, c(0.9, 1))"
## [1] 0.9761058
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              3550.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3550.9
## cut2(oasis, g = 3)                                              2   3663.7
## cut2(age, g = 3)                                                2   3552.8
## female                                                          1   3553.8
## request_tele                                                    1   3560.7
## request_vre                                                     1   3564.9
## request_cdiff                                                   1   3556.8
## cut2(elixhauser_hospital, g = 3)                                2   3652.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3651.0
## as.factor(callout_month)                                       11   3565.9
## as.factor(callout_year)                                         6   3569.7
## as.factor(callout_dayofweek)                                    6   3557.8
## as.factor(callout_wardid == 1)                                  1   3587.5
## MED_SERVICE                                                     1   3553.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3556.4
##                                                                   AIC
## <none>                                                         3638.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3636.9
## cut2(oasis, g = 3)                                             3747.7
## cut2(age, g = 3)                                               3636.8
## female                                                         3639.8
## request_tele                                                   3646.7
## request_vre                                                    3650.9
## request_cdiff                                                  3642.8
## cut2(elixhauser_hospital, g = 3)                               3736.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3731.0
## as.factor(callout_month)                                       3631.9
## as.factor(callout_year)                                        3645.7
## as.factor(callout_dayofweek)                                   3633.8
## as.factor(callout_wardid == 1)                                 3673.5
## MED_SERVICE                                                    3639.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3638.4
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.002
## cut2(oasis, g = 3)                                             112.818
## cut2(age, g = 3)                                                 1.971
## female                                                           2.904
## request_tele                                                     9.834
## request_vre                                                     14.061
## request_cdiff                                                    5.925
## cut2(elixhauser_hospital, g = 3)                               101.607
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     100.112
## as.factor(callout_month)                                        15.046
## as.factor(callout_year)                                         18.788
## as.factor(callout_dayofweek)                                     6.926
## as.factor(callout_wardid == 1)                                  36.657
## MED_SERVICE                                                      2.687
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.496
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.964406
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                                0.373183
## female                                                          0.088365
## request_tele                                                    0.001713
## request_vre                                                     0.000177
## request_cdiff                                                   0.014926
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.180405
## as.factor(callout_year)                                         0.004537
## as.factor(callout_dayofweek)                                    0.327704
## as.factor(callout_wardid == 1)                                 1.409e-09
## MED_SERVICE                                                     0.101144
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.138867
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                                  
## female                                                         .  
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  *  
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ** 
## as.factor(callout_dayofweek)                                      
## as.factor(callout_wardid == 1)                                 ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(age, g = 3)"
## [1] 0.3731832
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + female + request_tele + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              3552.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3552.8
## cut2(oasis, g = 3)                                              2   3687.3
## female                                                          1   3555.5
## request_tele                                                    1   3562.0
## request_vre                                                     1   3566.6
## request_cdiff                                                   1   3559.1
## cut2(elixhauser_hospital, g = 3)                                2   3662.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3651.1
## as.factor(callout_month)                                       11   3568.1
## as.factor(callout_year)                                         6   3571.5
## as.factor(callout_dayofweek)                                    6   3559.8
## as.factor(callout_wardid == 1)                                  1   3588.1
## MED_SERVICE                                                     1   3555.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3558.2
##                                                                   AIC
## <none>                                                         3636.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3634.8
## cut2(oasis, g = 3)                                             3767.3
## female                                                         3637.5
## request_tele                                                   3644.0
## request_vre                                                    3648.6
## request_cdiff                                                  3641.1
## cut2(elixhauser_hospital, g = 3)                               3742.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3727.1
## as.factor(callout_month)                                       3630.1
## as.factor(callout_year)                                        3643.5
## as.factor(callout_dayofweek)                                   3631.8
## as.factor(callout_wardid == 1)                                 3670.1
## MED_SERVICE                                                    3637.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3636.2
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.006
## cut2(oasis, g = 3)                                             134.412
## female                                                           2.674
## request_tele                                                     9.205
## request_vre                                                     13.798
## request_cdiff                                                    6.279
## cut2(elixhauser_hospital, g = 3)                               109.439
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      98.254
## as.factor(callout_month)                                        15.218
## as.factor(callout_year)                                         18.705
## as.factor(callout_dayofweek)                                     6.926
## as.factor(callout_wardid == 1)                                  35.263
## MED_SERVICE                                                      2.463
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.377
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.9407325
## cut2(oasis, g = 3)                                             < 2.2e-16
## female                                                         0.1020267
## request_tele                                                   0.0024130
## request_vre                                                    0.0002035
## request_cdiff                                                  0.0122157
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.1727489
## as.factor(callout_year)                                        0.0046927
## as.factor(callout_dayofweek)                                   0.3277516
## as.factor(callout_wardid == 1)                                  2.88e-09
## MED_SERVICE                                                    0.1165784
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1461829
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## female                                                            
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  *  
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ** 
## as.factor(callout_dayofweek)                                      
## as.factor(callout_wardid == 1)                                 ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_dayofweek)"
## [1] 0.3277516
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + female + request_tele + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              3559.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3559.8
## cut2(oasis, g = 3)                                              2   3694.3
## female                                                          1   3562.5
## request_tele                                                    1   3568.8
## request_vre                                                     1   3573.5
## request_cdiff                                                   1   3566.1
## cut2(elixhauser_hospital, g = 3)                                2   3669.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3657.8
## as.factor(callout_month)                                       11   3575.1
## as.factor(callout_year)                                         6   3578.8
## as.factor(callout_wardid == 1)                                  1   3594.8
## MED_SERVICE                                                     1   3562.0
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3565.4
##                                                                   AIC
## <none>                                                         3631.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3629.8
## cut2(oasis, g = 3)                                             3762.3
## female                                                         3632.5
## request_tele                                                   3638.8
## request_vre                                                    3643.5
## request_cdiff                                                  3636.1
## cut2(elixhauser_hospital, g = 3)                               3737.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3721.8
## as.factor(callout_month)                                       3625.1
## as.factor(callout_year)                                        3638.8
## as.factor(callout_wardid == 1)                                 3664.8
## MED_SERVICE                                                    3632.0
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3631.4
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.001
## cut2(oasis, g = 3)                                             134.520
## female                                                           2.710
## request_tele                                                     9.067
## request_vre                                                     13.730
## request_cdiff                                                    6.293
## cut2(elixhauser_hospital, g = 3)                               109.429
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      98.038
## as.factor(callout_month)                                        15.331
## as.factor(callout_year)                                         18.992
## as.factor(callout_wardid == 1)                                  35.029
## MED_SERVICE                                                      2.280
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.629
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.978514
## cut2(oasis, g = 3)                                             < 2.2e-16
## female                                                          0.099728
## request_tele                                                    0.002603
## request_vre                                                     0.000211
## request_cdiff                                                   0.012120
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.167834
## as.factor(callout_year)                                         0.004177
## as.factor(callout_wardid == 1)                                 3.249e-09
## MED_SERVICE                                                     0.131081
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.131151
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## female                                                         .  
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  *  
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ** 
## as.factor(callout_wardid == 1)                                 ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_month)"
## [1] 0.167834
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + female + request_tele + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid == 
##     1) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              3575.1
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3575.1
## cut2(oasis, g = 3)                                              2   3708.3
## female                                                          1   3577.9
## request_tele                                                    1   3584.4
## request_vre                                                     1   3588.0
## request_cdiff                                                   1   3581.8
## cut2(elixhauser_hospital, g = 3)                                2   3683.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3672.7
## as.factor(callout_year)                                         6   3591.4
## as.factor(callout_wardid == 1)                                  1   3611.0
## MED_SERVICE                                                     1   3577.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3581.1
##                                                                   AIC
## <none>                                                         3625.1
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3623.1
## cut2(oasis, g = 3)                                             3754.3
## female                                                         3625.9
## request_tele                                                   3632.4
## request_vre                                                    3636.0
## request_cdiff                                                  3629.8
## cut2(elixhauser_hospital, g = 3)                               3729.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3714.7
## as.factor(callout_year)                                        3629.4
## as.factor(callout_wardid == 1)                                 3659.0
## MED_SERVICE                                                    3625.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3625.1
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.040
## cut2(oasis, g = 3)                                             133.185
## female                                                           2.816
## request_tele                                                     9.266
## request_vre                                                     12.893
## request_cdiff                                                    6.656
## cut2(elixhauser_hospital, g = 3)                               108.500
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      97.573
## as.factor(callout_year)                                         16.343
## as.factor(callout_wardid == 1)                                  35.911
## MED_SERVICE                                                      2.502
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.977
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.8406641
## cut2(oasis, g = 3)                                             < 2.2e-16
## female                                                         0.0933013
## request_tele                                                   0.0023341
## request_vre                                                    0.0003297
## request_cdiff                                                  0.0098843
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_year)                                        0.0120256
## as.factor(callout_wardid == 1)                                 2.066e-09
## MED_SERVICE                                                    0.1136967
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1127364
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## female                                                         .  
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_year)                                        *  
## as.factor(callout_wardid == 1)                                 ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "MED_SERVICE"
## [1] 0.1136967
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + female + request_tele + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid == 
##     1) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              3577.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   3577.6
## cut2(oasis, g = 3)                                              2   3714.3
## female                                                          1   3580.5
## request_tele                                                    1   3587.0
## request_vre                                                     1   3590.7
## request_cdiff                                                   1   3584.2
## cut2(elixhauser_hospital, g = 3)                                2   3688.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   3673.1
## as.factor(callout_year)                                         6   3594.4
## as.factor(callout_wardid == 1)                                  1   3611.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   3583.6
##                                                                   AIC
## <none>                                                         3625.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    3623.6
## cut2(oasis, g = 3)                                             3758.3
## female                                                         3626.5
## request_tele                                                   3633.0
## request_vre                                                    3636.7
## request_cdiff                                                  3630.2
## cut2(elixhauser_hospital, g = 3)                               3732.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     3713.1
## as.factor(callout_year)                                        3630.4
## as.factor(callout_wardid == 1)                                 3657.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3625.6
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.035
## cut2(oasis, g = 3)                                             136.731
## female                                                           2.927
## request_tele                                                     9.378
## request_vre                                                     13.049
## request_cdiff                                                    6.645
## cut2(elixhauser_hospital, g = 3)                               110.350
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      95.537
## as.factor(callout_year)                                         16.830
## as.factor(callout_wardid == 1)                                  33.555
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   5.995
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.8509339
## cut2(oasis, g = 3)                                             < 2.2e-16
## female                                                         0.0871324
## request_tele                                                   0.0021962
## request_vre                                                    0.0003034
## request_cdiff                                                  0.0099458
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_year)                                        0.0099270
## as.factor(callout_wardid == 1)                                 6.927e-09
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1118491
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## female                                                         .  
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_year)                                        ** 
## as.factor(callout_wardid == 1)                                 ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "relevel(cut2(hourofcallout2, c(7, 12, 19)), \"[ 7.000,12.000)\")"
## [1] 0.1118491
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + female + request_tele + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid == 
##     1)
##                                                             Df Deviance
## <none>                                                           3583.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1   3583.6
## cut2(oasis, g = 3)                                           2   3721.5
## female                                                       1   3586.6
## request_tele                                                 1   3592.8
## request_vre                                                  1   3597.0
## request_cdiff                                                1   3590.0
## cut2(elixhauser_hospital, g = 3)                             2   3694.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4   3679.6
## as.factor(callout_year)                                      6   3599.5
## as.factor(callout_wardid == 1)                               1   3616.9
##                                                                AIC     LRT
## <none>                                                      3625.6        
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3623.6   0.017
## cut2(oasis, g = 3)                                          3759.5 137.900
## female                                                      3626.6   3.008
## request_tele                                                3632.8   9.228
## request_vre                                                 3637.0  13.370
## request_cdiff                                               3630.0   6.408
## cut2(elixhauser_hospital, g = 3)                            3732.0 110.384
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  3713.6  95.997
## as.factor(callout_year)                                     3629.5  15.950
## as.factor(callout_wardid == 1)                              3656.9  33.293
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8960330    
## cut2(oasis, g = 3)                                          < 2.2e-16 ***
## female                                                      0.0828467 .  
## request_tele                                                0.0023833 ** 
## request_vre                                                 0.0002557 ***
## request_cdiff                                               0.0113599 *  
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_year)                                     0.0140253 *  
## as.factor(callout_wardid == 1)                              7.928e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "female"
## [1] 0.08284666
## Single term deletions
## 
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") + 
##     cut2(oasis, g = 3) + request_tele + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid == 
##     1)
##                                                             Df Deviance
## <none>                                                           3586.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1   3586.6
## cut2(oasis, g = 3)                                           2   3723.0
## request_tele                                                 1   3596.0
## request_vre                                                  1   3599.6
## request_cdiff                                                1   3592.9
## cut2(elixhauser_hospital, g = 3)                             2   3700.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4   3682.2
## as.factor(callout_year)                                      6   3602.3
## as.factor(callout_wardid == 1)                               1   3619.9
##                                                                AIC     LRT
## <none>                                                      3626.6        
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3624.6   0.004
## cut2(oasis, g = 3)                                          3759.0 136.401
## request_tele                                                3634.0   9.429
## request_vre                                                 3637.6  13.006
## request_cdiff                                               3630.9   6.296
## cut2(elixhauser_hospital, g = 3)                            3736.3 113.710
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  3714.2  95.582
## as.factor(callout_year)                                     3630.3  15.648
## as.factor(callout_wardid == 1)                              3657.9  33.251
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9477193    
## cut2(oasis, g = 3)                                          < 2.2e-16 ***
## request_tele                                                0.0021362 ** 
## request_vre                                                 0.0003105 ***
## request_cdiff                                               0.0121009 *  
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_year)                                     0.0157712 *  
## as.factor(callout_wardid == 1)                              8.101e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_year)"
## [1] 0.01577116
    hospitaldeath
    Odds Ratio CI p
(Intercept)   0.02 0.01 – 0.04 <.001
I(cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]”)   0.99 0.74 – 1.31 .948
cut2(oasis, g = 3)
[27,34)   2.03 1.54 – 2.71 <.001
[34,64]   4.20 3.23 – 5.52 <.001
request_tele   0.74 0.60 – 0.90 .002
request_vre   1.85 1.33 – 2.51 <.001
request_cdiff   1.53 1.10 – 2.09 .009
cut2(elixhauser_hospital, g = 3)
[ 1, 7)   2.14 1.60 – 2.89 <.001
[ 7,31]   3.76 2.89 – 4.96 <.001
cut2(los_pre_callout_days, c(1, 3, 7, 28))
[ 1.000, 3.000)   0.92 0.71 – 1.21 .550
[ 3.000, 7.000)   1.65 1.25 – 2.17 <.001
[ 7.000, 28.000)   2.31 1.74 – 3.09 <.001
[ 28.000,130.762]   10.14 5.32 – 18.99 <.001
as.factor(callout_year)
2006   0.79 0.50 – 1.27 .320
2007   0.70 0.45 – 1.12 .128
2008   0.55 0.35 – 0.88 .010
2009   0.50 0.32 – 0.80 .003
2010   0.64 0.41 – 1.01 .049
2011   0.52 0.33 – 0.83 .005
as.factor(callout_wardid == 1) (TRUE)   0.51 0.41 – 0.63 <.001
Observations   9673
##                                                                       
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE  0.991
## cut2(oasis, g = 3)[27,34)                                        2.034
## cut2(oasis, g = 3)[34,64]                                        4.199
## request_tele                                                     0.736
## request_vre                                                      1.848
## request_cdiff                                                    1.531
## cut2(elixhauser_hospital, g = 3)[  1, 7)                         2.142
## cut2(elixhauser_hospital, g = 3)[  7,31]                         3.759
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)      0.922
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)      1.646
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)      2.314
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     10.140
## as.factor(callout_year)2006                                      0.790
## as.factor(callout_year)2007                                      0.704
## as.factor(callout_year)2008                                      0.547
## as.factor(callout_year)2009                                      0.501
## as.factor(callout_year)2010                                      0.638
## as.factor(callout_year)2011                                      0.520
## as.factor(callout_wardid == 1)TRUE                               0.506
##                                                                 2.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.740
## cut2(oasis, g = 3)[27,34)                                       1.542
## cut2(oasis, g = 3)[34,64]                                       3.230
## request_tele                                                    0.601
## request_vre                                                     1.335
## request_cdiff                                                   1.101
## cut2(elixhauser_hospital, g = 3)[  1, 7)                        1.604
## cut2(elixhauser_hospital, g = 3)[  7,31]                        2.888
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)     0.709
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)     1.252
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)     1.739
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     5.319
## as.factor(callout_year)2006                                     0.502
## as.factor(callout_year)2007                                     0.453
## as.factor(callout_year)2008                                     0.349
## as.factor(callout_year)2009                                     0.320
## as.factor(callout_year)2010                                     0.412
## as.factor(callout_year)2011                                     0.332
## as.factor(callout_wardid == 1)TRUE                              0.406
##                                                                 97.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE  1.309
## cut2(oasis, g = 3)[27,34)                                        2.705
## cut2(oasis, g = 3)[34,64]                                        5.520
## request_tele                                                     0.896
## request_vre                                                      2.514
## request_cdiff                                                    2.091
## cut2(elixhauser_hospital, g = 3)[  1, 7)                         2.887
## cut2(elixhauser_hospital, g = 3)[  7,31]                         4.956
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)      1.206
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)      2.170
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)      3.087
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     18.993
## as.factor(callout_year)2006                                      1.272
## as.factor(callout_year)2007                                      1.121
## as.factor(callout_year)2008                                      0.877
## as.factor(callout_year)2009                                      0.803
## as.factor(callout_year)2010                                      1.013
## as.factor(callout_year)2011                                      0.834
## as.factor(callout_wardid == 1)TRUE                               0.634
##                                                                      
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.948
## cut2(oasis, g = 3)[27,34)                                       0.000
## cut2(oasis, g = 3)[34,64]                                       0.000
## request_tele                                                    0.002
## request_vre                                                     0.000
## request_cdiff                                                   0.009
## cut2(elixhauser_hospital, g = 3)[  1, 7)                        0.000
## cut2(elixhauser_hospital, g = 3)[  7,31]                        0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)     0.550
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)     0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)     0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     0.000
## as.factor(callout_year)2006                                     0.320
## as.factor(callout_year)2007                                     0.128
## as.factor(callout_year)2008                                     0.010
## as.factor(callout_year)2009                                     0.003
## as.factor(callout_year)2010                                     0.049
## as.factor(callout_year)2011                                     0.005
## as.factor(callout_wardid == 1)TRUE                              0.000

Answer 2: After adjusting for potential confounders, there is no statistically significant evidence that a long delay produces a better hospital mortality outcome.

Question 3: Do individuals with a long discharge delay have smaller numbers of hospital free days?

HFDs are technically ordinal, so we tried Proportional Odds Logistic Regression. This didn’t make it into the paper.

There is not very much evidence in the above table that hospital free days is impacted by long discharge delays (21.36 vs 21.09, p=0.265)

We can look at it using an empirical cumulative distribution function:

Question 4: Do people who have long discharge delays (>24 hours) have a “good outcome” defined as a short post discharge LOS (<1 week) and survive?

## 
## FALSE  TRUE 
##  2583  7090
## 
## FALSE  TRUE 
##  8662  1011
##        
##         FALSE TRUE
##   FALSE  2282  301
##   TRUE   6380  710
##        
##             FALSE      TRUE
##   FALSE 0.2634495 0.2977250
##   TRUE  0.7365505 0.7022750
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  with(d, table(HOSP_FREE_DAYS > 21, cut2(DISCHARGEDELAY_HOURS,     c(24)) == "[ 24.000,129.566]"))
## X-squared = 5.2604, df = 1, p-value = 0.02182
Patient Characteristics Overall
variable_name level FALSE TRUE p test
n 2583 7090
micu (%) 1 2583 (100.0) 7090 (100.0) NA
age (mean (sd)) 64.83 (16.45) 63.26 (18.44) <0.001
callout_month (%) 1 202 ( 7.8) 572 ( 8.1) 0.783
2 198 ( 7.7) 540 ( 7.6)
3 191 ( 7.4) 542 ( 7.6)
4 219 ( 8.5) 507 ( 7.2)
5 210 ( 8.1) 584 ( 8.2)
6 199 ( 7.7) 546 ( 7.7)
7 207 ( 8.0) 572 ( 8.1)
8 231 ( 8.9) 646 ( 9.1)
9 240 ( 9.3) 611 ( 8.6)
10 241 ( 9.3) 673 ( 9.5)
11 207 ( 8.0) 616 ( 8.7)
12 238 ( 9.2) 681 ( 9.6)
female (%) 0 1353 ( 52.4) 3603 ( 50.8) 0.181
1 1230 ( 47.6) 3487 ( 49.2)
request_tele (%) 0 1674 ( 64.8) 4644 ( 65.5) 0.543
1 909 ( 35.2) 2446 ( 34.5)
request_resp (%) 0 2538 ( 98.3) 6976 ( 98.4) 0.712
1 45 ( 1.7) 114 ( 1.6)
request_cdiff (%) 0 2402 ( 93.0) 6788 ( 95.7) <0.001
1 181 ( 7.0) 302 ( 4.3)
request_mrsa (%) 0 2193 ( 84.9) 6212 ( 87.6) 0.001
1 390 ( 15.1) 878 ( 12.4)
request_vre (%) 0 2377 ( 92.0) 6805 ( 96.0) <0.001
1 206 ( 8.0) 285 ( 4.0)
oasis (mean (sd)) 31.12 (7.57) 29.37 (7.14) <0.001
elixhauser_hospital (mean (sd)) 5.58 (7.46) 2.80 (7.06) <0.001
ethnicity (%) White 1857 ( 71.9) 5053 ( 71.3) 0.201
African American/Black 373 ( 14.4) 1121 ( 15.8)
Other 353 ( 13.7) 916 ( 12.9)
MED_SERVICE (%) FALSE 208 ( 8.1) 484 ( 6.8) 0.043
TRUE 2375 ( 91.9) 6606 ( 93.2)
HOSP_FREE_DAYS (mean (sd)) 11.42 (8.08) 24.95 (1.79) <0.001
callout_dayofweek (%) friday 382 ( 14.8) 1060 ( 15.0) 0.324
monday 363 ( 14.1) 937 ( 13.2)
saturday 312 ( 12.1) 980 ( 13.8)
sunday 345 ( 13.4) 922 ( 13.0)
thursday 374 ( 14.5) 1038 ( 14.6)
tuesday 376 ( 14.6) 1046 ( 14.8)
wednesday 431 ( 16.7) 1107 ( 15.6)
CALLOUT_DURING_NIGHT (%) FALSE 2560 ( 99.1) 7047 ( 99.4) 0.173
TRUE 23 ( 0.9) 43 ( 0.6)
CALLOUT_DURING_ROUNDS (%) FALSE 1070 ( 41.4) 2746 ( 38.7) 0.018
TRUE 1513 ( 58.6) 4344 ( 61.3)
DISCHARGEDELAY_HOURS (mean (sd)) 10.71 (10.75) 10.25 (10.10) 0.053
hourofcallout2 (median [IQR]) 11.53 [10.27, 13.25] 11.35 [10.05, 13.13] <0.001 nonnorm
PROPFULL_BEDS (mean (sd)) 0.91 (0.09) 0.91 (0.09) 0.677
postcalldaycat2 (%) 0 2045 ( 79.2) 5755 ( 81.2) 0.030
[1,5] 538 ( 20.8) 1335 ( 18.8)
los_preicu_days (median [IQR]) 0.00 [0.00, 0.82] 0.00 [0.00, 0.07] <0.001 nonnorm
los_post_callout_days (median [IQR]) 11.25 [8.36, 16.27] 3.21 [2.14, 4.99] <0.001 nonnorm
los_post_icu_days (median [IQR]) 10.89 [8.00, 15.92] 2.85 [1.79, 4.25] <0.001 nonnorm
los_pre_callout_days (median [IQR]) 2.73 [1.42, 6.49] 1.64 [0.87, 2.97] <0.001 nonnorm
callout_year (%) 2005 114 ( 4.4) 262 ( 3.7) 0.001
2006 329 ( 12.7) 741 ( 10.5)
2007 405 ( 15.7) 1012 ( 14.3)
2008 409 ( 15.8) 1229 ( 17.3)
2009 420 ( 16.3) 1248 ( 17.6)
2010 474 ( 18.4) 1265 ( 17.8)
2011 432 ( 16.7) 1333 ( 18.8)
hospitaldeath (mean (sd)) 0.21 (0.40) 0.00 (0.00) <0.001
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10335
## cut2(oasis, g = 3)                                              2    10371
## cut2(age, g = 3)                                                2    10342
## female                                                          1    10336
## request_tele                                                    1    10336
## request_resp                                                    1    10338
## request_mrsa                                                    1    10337
## request_vre                                                     1    10365
## request_cdiff                                                   1    10346
## cut2(elixhauser_hospital, g = 3)                                2    10467
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10683
## as.factor(callout_month)                                       11    10349
## as.factor(callout_year)                                         6    10364
## as.factor(callout_dayofweek)                                    6    10340
## MED_SERVICE                                                     1    10335
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10340
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2    10335
##                                                                  AIC
## <none>                                                         10435
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10433
## cut2(oasis, g = 3)                                             10467
## cut2(age, g = 3)                                               10438
## female                                                         10434
## request_tele                                                   10434
## request_resp                                                   10436
## request_mrsa                                                   10435
## request_vre                                                    10463
## request_cdiff                                                  10444
## cut2(elixhauser_hospital, g = 3)                               10563
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10775
## as.factor(callout_month)                                       10427
## as.factor(callout_year)                                        10452
## as.factor(callout_dayofweek)                                   10428
## MED_SERVICE                                                    10433
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10434
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  10431
##                                                                   LRT
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.10
## cut2(oasis, g = 3)                                              35.86
## cut2(age, g = 3)                                                 6.90
## female                                                           0.52
## request_tele                                                     0.19
## request_resp                                                     3.17
## request_mrsa                                                     2.08
## request_vre                                                     30.09
## request_cdiff                                                   10.58
## cut2(elixhauser_hospital, g = 3)                               131.80
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     348.11
## as.factor(callout_month)                                        13.82
## as.factor(callout_year)                                         29.07
## as.factor(callout_dayofweek)                                     4.64
## MED_SERVICE                                                      0.02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   4.52
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    0.12
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.755910
## cut2(oasis, g = 3)                                             1.637e-08
## cut2(age, g = 3)                                                0.031693
## female                                                          0.471577
## request_tele                                                    0.658857
## request_resp                                                    0.074821
## request_mrsa                                                    0.149049
## request_vre                                                    4.124e-08
## request_cdiff                                                   0.001141
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.242854
## as.factor(callout_year)                                        5.909e-05
## as.factor(callout_dayofweek)                                    0.591136
## MED_SERVICE                                                     0.877728
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.210127
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   0.942011
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               *  
## female                                                            
## request_tele                                                      
## request_resp                                                   .  
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                      
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + request_vre + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_wardid == 1)
##                                                             Df Deviance
## <none>                                                            10404
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1    10407
## cut2(oasis, g = 3)                                           2    10438
## request_vre                                                  1    10441
## cut2(elixhauser_hospital, g = 3)                             2    10542
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4    10775
## as.factor(callout_wardid == 1)                               1    10466
##                                                               AIC    LRT
## <none>                                                      10428       
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10429   2.27
## cut2(oasis, g = 3)                                          10458  33.33
## request_vre                                                 10463  36.09
## cut2(elixhauser_hospital, g = 3)                            10562 137.49
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  10791 370.45
## as.factor(callout_wardid == 1)                              10488  61.52
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.1323    
## cut2(oasis, g = 3)                                          5.779e-08 ***
## request_vre                                                 1.880e-09 ***
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_wardid == 1)                              4.390e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid == 
##     1)
##                                                             Df Deviance
## <none>                                                            10360
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1    10360
## cut2(oasis, g = 3)                                           2    10396
## cut2(age, g = 3)                                             2    10367
## request_resp                                                 1    10362
## request_mrsa                                                 1    10362
## request_vre                                                  1    10390
## request_cdiff                                                1    10370
## cut2(elixhauser_hospital, g = 3)                             2    10494
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4    10710
## as.factor(callout_year)                                      6    10384
## as.factor(callout_wardid == 1)                               1    10426
##                                                               AIC    LRT
## <none>                                                      10406       
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10404   0.01
## cut2(oasis, g = 3)                                          10438  36.04
## cut2(age, g = 3)                                            10409   7.02
## request_resp                                                10406   2.57
## request_mrsa                                                10406   2.16
## request_vre                                                 10434  30.78
## request_cdiff                                               10414  10.60
## cut2(elixhauser_hospital, g = 3)                            10536 134.19
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  10748 350.35
## as.factor(callout_year)                                     10418  24.90
## as.factor(callout_wardid == 1)                              10470  65.97
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9128998    
## cut2(oasis, g = 3)                                          1.489e-08 ***
## cut2(age, g = 3)                                            0.0298308 *  
## request_resp                                                0.1089144    
## request_mrsa                                                0.1416814    
## request_vre                                                 2.884e-08 ***
## request_cdiff                                               0.0011290 ** 
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_year)                                     0.0003568 ***
## as.factor(callout_wardid == 1)                              4.580e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## glm(formula = I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, 
##     c(24)) == "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, 
##     g = 3) + female + request_tele + request_resp + request_mrsa + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9, 
##         1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 
##     12, 19)), "[ 7.000,12.000)"), family = "binomial", data = d)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1794  -0.9757   0.6086   0.7738   1.9107  
## 
## Coefficients:
##                                                                                  Estimate
## (Intercept)                                                                     1.242e+00
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                -2.538e-02
## cut2(oasis, g = 3)[27,34)                                                      -1.931e-01
## cut2(oasis, g = 3)[34,64]                                                      -3.924e-01
## cut2(age, g = 3)[56.1,73.8)                                                    -2.386e-02
## cut2(age, g = 3)[73.8,91.4]                                                     1.293e-01
## female                                                                          3.514e-02
## request_tele                                                                   -2.267e-02
## request_resp                                                                   -3.399e-01
## request_mrsa                                                                   -1.028e-01
## request_vre                                                                    -5.711e-01
## request_cdiff                                                                  -3.396e-01
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                       -3.731e-01
## cut2(elixhauser_hospital, g = 3)[  7,31]                                       -7.008e-01
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                    -1.586e-01
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                    -6.209e-01
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                    -1.314e+00
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                    -2.088e+00
## as.factor(callout_month)2                                                      -4.175e-02
## as.factor(callout_month)3                                                       7.850e-02
## as.factor(callout_month)4                                                      -1.967e-01
## as.factor(callout_month)5                                                       4.161e-02
## as.factor(callout_month)6                                                      -3.685e-03
## as.factor(callout_month)7                                                       2.546e-02
## as.factor(callout_month)8                                                       7.958e-06
## as.factor(callout_month)9                                                      -5.039e-02
## as.factor(callout_month)10                                                      5.643e-02
## as.factor(callout_month)11                                                      2.117e-01
## as.factor(callout_month)12                                                      7.454e-02
## as.factor(callout_year)2006                                                     2.643e-02
## as.factor(callout_year)2007                                                     1.661e-01
## as.factor(callout_year)2008                                                     3.300e-01
## as.factor(callout_year)2009                                                     4.001e-01
## as.factor(callout_year)2010                                                     2.483e-01
## as.factor(callout_year)2011                                                     4.167e-01
## as.factor(callout_dayofweek)monday                                             -8.049e-02
## as.factor(callout_dayofweek)saturday                                            8.648e-02
## as.factor(callout_dayofweek)sunday                                             -6.593e-03
## as.factor(callout_dayofweek)thursday                                           -6.554e-03
## as.factor(callout_dayofweek)tuesday                                            -2.928e-02
## as.factor(callout_dayofweek)wednesday                                          -1.125e-01
## as.factor(callout_wardid == 1)TRUE                                              5.195e-01
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                     4.825e-02
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                     1.842e-01
## MED_SERVICETRUE                                                                 1.481e-02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)  -5.123e-01
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)  -5.835e-02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]  -1.137e-01
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)  3.136e-02
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -2.819e-02
##                                                                                Std. Error
## (Intercept)                                                                     2.200e-01
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                 8.155e-02
## cut2(oasis, g = 3)[27,34)                                                       6.049e-02
## cut2(oasis, g = 3)[34,64]                                                       6.559e-02
## cut2(age, g = 3)[56.1,73.8)                                                     6.110e-02
## cut2(age, g = 3)[73.8,91.4]                                                     6.578e-02
## female                                                                          4.881e-02
## request_tele                                                                    5.134e-02
## request_resp                                                                    1.867e-01
## request_mrsa                                                                    7.092e-02
## request_vre                                                                     1.025e-01
## request_cdiff                                                                   1.031e-01
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                        6.284e-02
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        6.154e-02
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                     6.448e-02
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                     7.319e-02
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                     8.237e-02
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                     3.237e-01
## as.factor(callout_month)2                                                       1.229e-01
## as.factor(callout_month)3                                                       1.241e-01
## as.factor(callout_month)4                                                       1.213e-01
## as.factor(callout_month)5                                                       1.212e-01
## as.factor(callout_month)6                                                       1.228e-01
## as.factor(callout_month)7                                                       1.215e-01
## as.factor(callout_month)8                                                       1.187e-01
## as.factor(callout_month)9                                                       1.188e-01
## as.factor(callout_month)10                                                      1.182e-01
## as.factor(callout_month)11                                                      1.221e-01
## as.factor(callout_month)12                                                      1.193e-01
## as.factor(callout_year)2006                                                     1.400e-01
## as.factor(callout_year)2007                                                     1.374e-01
## as.factor(callout_year)2008                                                     1.367e-01
## as.factor(callout_year)2009                                                     1.374e-01
## as.factor(callout_year)2010                                                     1.361e-01
## as.factor(callout_year)2011                                                     1.362e-01
## as.factor(callout_dayofweek)monday                                              9.098e-02
## as.factor(callout_dayofweek)saturday                                            9.959e-02
## as.factor(callout_dayofweek)sunday                                              9.856e-02
## as.factor(callout_dayofweek)thursday                                            9.181e-02
## as.factor(callout_dayofweek)tuesday                                             9.138e-02
## as.factor(callout_dayofweek)wednesday                                           9.020e-02
## as.factor(callout_wardid == 1)TRUE                                              1.033e-01
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                     1.330e-01
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                     1.803e-01
## MED_SERVICETRUE                                                                 9.621e-02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)   2.797e-01
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)   5.090e-02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]   1.660e-01
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)  1.390e-01
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]  1.879e-01
##                                                                                z value
## (Intercept)                                                                      5.645
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                 -0.311
## cut2(oasis, g = 3)[27,34)                                                       -3.192
## cut2(oasis, g = 3)[34,64]                                                       -5.982
## cut2(age, g = 3)[56.1,73.8)                                                     -0.390
## cut2(age, g = 3)[73.8,91.4]                                                      1.966
## female                                                                           0.720
## request_tele                                                                    -0.442
## request_resp                                                                    -1.820
## request_mrsa                                                                    -1.449
## request_vre                                                                     -5.570
## request_cdiff                                                                   -3.293
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                        -5.936
## cut2(elixhauser_hospital, g = 3)[  7,31]                                       -11.388
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                     -2.460
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                     -8.483
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                    -15.949
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                     -6.449
## as.factor(callout_month)2                                                       -0.340
## as.factor(callout_month)3                                                        0.633
## as.factor(callout_month)4                                                       -1.622
## as.factor(callout_month)5                                                        0.343
## as.factor(callout_month)6                                                       -0.030
## as.factor(callout_month)7                                                        0.210
## as.factor(callout_month)8                                                        0.000
## as.factor(callout_month)9                                                       -0.424
## as.factor(callout_month)10                                                       0.477
## as.factor(callout_month)11                                                       1.733
## as.factor(callout_month)12                                                       0.625
## as.factor(callout_year)2006                                                      0.189
## as.factor(callout_year)2007                                                      1.209
## as.factor(callout_year)2008                                                      2.413
## as.factor(callout_year)2009                                                      2.913
## as.factor(callout_year)2010                                                      1.825
## as.factor(callout_year)2011                                                      3.060
## as.factor(callout_dayofweek)monday                                              -0.885
## as.factor(callout_dayofweek)saturday                                             0.868
## as.factor(callout_dayofweek)sunday                                              -0.067
## as.factor(callout_dayofweek)thursday                                            -0.071
## as.factor(callout_dayofweek)tuesday                                             -0.320
## as.factor(callout_dayofweek)wednesday                                           -1.248
## as.factor(callout_wardid == 1)TRUE                                               5.028
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                      0.363
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                      1.022
## MED_SERVICETRUE                                                                  0.154
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)   -1.832
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)   -1.146
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]   -0.685
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)   0.226
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]  -0.150
##                                                                                Pr(>|z|)
## (Intercept)                                                                    1.65e-08
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                 0.75568
## cut2(oasis, g = 3)[27,34)                                                       0.00141
## cut2(oasis, g = 3)[34,64]                                                      2.20e-09
## cut2(age, g = 3)[56.1,73.8)                                                     0.69620
## cut2(age, g = 3)[73.8,91.4]                                                     0.04934
## female                                                                          0.47161
## request_tele                                                                    0.65872
## request_resp                                                                    0.06873
## request_mrsa                                                                    0.14733
## request_vre                                                                    2.55e-08
## request_cdiff                                                                   0.00099
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                       2.91e-09
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                     0.01391
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                     < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                     < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                    1.13e-10
## as.factor(callout_month)2                                                       0.73397
## as.factor(callout_month)3                                                       0.52699
## as.factor(callout_month)4                                                       0.10485
## as.factor(callout_month)5                                                       0.73130
## as.factor(callout_month)6                                                       0.97607
## as.factor(callout_month)7                                                       0.83405
## as.factor(callout_month)8                                                       0.99995
## as.factor(callout_month)9                                                       0.67155
## as.factor(callout_month)10                                                      0.63301
## as.factor(callout_month)11                                                      0.08303
## as.factor(callout_month)12                                                      0.53207
## as.factor(callout_year)2006                                                     0.85019
## as.factor(callout_year)2007                                                     0.22663
## as.factor(callout_year)2008                                                     0.01580
## as.factor(callout_year)2009                                                     0.00358
## as.factor(callout_year)2010                                                     0.06806
## as.factor(callout_year)2011                                                     0.00222
## as.factor(callout_dayofweek)monday                                              0.37630
## as.factor(callout_dayofweek)saturday                                            0.38520
## as.factor(callout_dayofweek)sunday                                              0.94666
## as.factor(callout_dayofweek)thursday                                            0.94309
## as.factor(callout_dayofweek)tuesday                                             0.74862
## as.factor(callout_dayofweek)wednesday                                           0.21216
## as.factor(callout_wardid == 1)TRUE                                             4.96e-07
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                     0.71671
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                     0.30697
## MED_SERVICETRUE                                                                 0.87765
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)   0.06698
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)   0.25166
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]   0.49342
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)  0.82156
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]  0.88075
##                                                                                   
## (Intercept)                                                                    ***
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                   
## cut2(oasis, g = 3)[27,34)                                                      ** 
## cut2(oasis, g = 3)[34,64]                                                      ***
## cut2(age, g = 3)[56.1,73.8)                                                       
## cut2(age, g = 3)[73.8,91.4]                                                    *  
## female                                                                            
## request_tele                                                                      
## request_resp                                                                   .  
## request_mrsa                                                                      
## request_vre                                                                    ***
## request_cdiff                                                                  ***
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                       ***
## cut2(elixhauser_hospital, g = 3)[  7,31]                                       ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                    *  
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                    ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                    ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                    ***
## as.factor(callout_month)2                                                         
## as.factor(callout_month)3                                                         
## as.factor(callout_month)4                                                         
## as.factor(callout_month)5                                                         
## as.factor(callout_month)6                                                         
## as.factor(callout_month)7                                                         
## as.factor(callout_month)8                                                         
## as.factor(callout_month)9                                                         
## as.factor(callout_month)10                                                        
## as.factor(callout_month)11                                                     .  
## as.factor(callout_month)12                                                        
## as.factor(callout_year)2006                                                       
## as.factor(callout_year)2007                                                       
## as.factor(callout_year)2008                                                    *  
## as.factor(callout_year)2009                                                    ** 
## as.factor(callout_year)2010                                                    .  
## as.factor(callout_year)2011                                                    ** 
## as.factor(callout_dayofweek)monday                                                
## as.factor(callout_dayofweek)saturday                                              
## as.factor(callout_dayofweek)sunday                                                
## as.factor(callout_dayofweek)thursday                                              
## as.factor(callout_dayofweek)tuesday                                               
## as.factor(callout_dayofweek)wednesday                                             
## as.factor(callout_wardid == 1)TRUE                                             ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                       
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                       
## MED_SERVICETRUE                                                                   
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)  .  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)     
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]     
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)    
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 11226  on 9672  degrees of freedom
## Residual deviance: 10335  on 9623  degrees of freedom
## AIC: 10435
## 
## Number of Fisher Scoring iterations: 4
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10335
## cut2(oasis, g = 3)                                              2    10371
## cut2(age, g = 3)                                                2    10342
## female                                                          1    10336
## request_tele                                                    1    10336
## request_resp                                                    1    10338
## request_mrsa                                                    1    10337
## request_vre                                                     1    10365
## request_cdiff                                                   1    10346
## cut2(elixhauser_hospital, g = 3)                                2    10467
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10683
## as.factor(callout_month)                                       11    10349
## as.factor(callout_year)                                         6    10364
## as.factor(callout_dayofweek)                                    6    10340
## MED_SERVICE                                                     1    10335
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10340
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2    10335
##                                                                  AIC
## <none>                                                         10435
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10433
## cut2(oasis, g = 3)                                             10467
## cut2(age, g = 3)                                               10438
## female                                                         10434
## request_tele                                                   10434
## request_resp                                                   10436
## request_mrsa                                                   10435
## request_vre                                                    10463
## request_cdiff                                                  10444
## cut2(elixhauser_hospital, g = 3)                               10563
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10775
## as.factor(callout_month)                                       10427
## as.factor(callout_year)                                        10452
## as.factor(callout_dayofweek)                                   10428
## MED_SERVICE                                                    10433
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10434
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  10431
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10335
## cut2(oasis, g = 3)                                              2    10371
## cut2(age, g = 3)                                                2    10342
## female                                                          1    10336
## request_tele                                                    1    10336
## request_resp                                                    1    10338
## request_mrsa                                                    1    10337
## request_vre                                                     1    10365
## request_cdiff                                                   1    10346
## cut2(elixhauser_hospital, g = 3)                                2    10467
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10683
## as.factor(callout_month)                                       11    10349
## as.factor(callout_year)                                         6    10364
## as.factor(callout_dayofweek)                                    6    10340
## MED_SERVICE                                                     1    10335
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10340
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2    10335
##                                                                  AIC
## <none>                                                         10435
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10433
## cut2(oasis, g = 3)                                             10467
## cut2(age, g = 3)                                               10438
## female                                                         10434
## request_tele                                                   10434
## request_resp                                                   10436
## request_mrsa                                                   10435
## request_vre                                                    10463
## request_cdiff                                                  10444
## cut2(elixhauser_hospital, g = 3)                               10563
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10775
## as.factor(callout_month)                                       10427
## as.factor(callout_year)                                        10452
## as.factor(callout_dayofweek)                                   10428
## MED_SERVICE                                                    10433
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10434
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  10431
##                                                                   LRT
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.10
## cut2(oasis, g = 3)                                              35.86
## cut2(age, g = 3)                                                 6.90
## female                                                           0.52
## request_tele                                                     0.19
## request_resp                                                     3.17
## request_mrsa                                                     2.08
## request_vre                                                     30.09
## request_cdiff                                                   10.58
## cut2(elixhauser_hospital, g = 3)                               131.80
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     348.11
## as.factor(callout_month)                                        13.82
## as.factor(callout_year)                                         29.07
## as.factor(callout_dayofweek)                                     4.64
## MED_SERVICE                                                      0.02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   4.52
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    0.12
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.755910
## cut2(oasis, g = 3)                                             1.637e-08
## cut2(age, g = 3)                                                0.031693
## female                                                          0.471577
## request_tele                                                    0.658857
## request_resp                                                    0.074821
## request_mrsa                                                    0.149049
## request_vre                                                    4.124e-08
## request_cdiff                                                   0.001141
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.242854
## as.factor(callout_year)                                        5.909e-05
## as.factor(callout_dayofweek)                                    0.591136
## MED_SERVICE                                                     0.877728
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.210127
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   0.942011
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               *  
## female                                                            
## request_tele                                                      
## request_resp                                                   .  
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                      
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))"
## [1] 0.942011
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10336
## cut2(oasis, g = 3)                                              2    10371
## cut2(age, g = 3)                                                2    10342
## female                                                          1    10336
## request_tele                                                    1    10336
## request_resp                                                    1    10338
## request_mrsa                                                    1    10338
## request_vre                                                     1    10366
## request_cdiff                                                   1    10346
## cut2(elixhauser_hospital, g = 3)                                2    10468
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10684
## as.factor(callout_month)                                       11    10349
## as.factor(callout_year)                                         6    10364
## as.factor(callout_dayofweek)                                    6    10340
## as.factor(callout_wardid == 1)                                  1    10396
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  2    10338
## MED_SERVICE                                                     1    10335
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10340
##                                                                  AIC
## <none>                                                         10431
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10430
## cut2(oasis, g = 3)                                             10463
## cut2(age, g = 3)                                               10434
## female                                                         10430
## request_tele                                                   10430
## request_resp                                                   10432
## request_mrsa                                                   10432
## request_vre                                                    10460
## request_cdiff                                                  10440
## cut2(elixhauser_hospital, g = 3)                               10560
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10772
## as.factor(callout_month)                                       10423
## as.factor(callout_year)                                        10448
## as.factor(callout_dayofweek)                                   10424
## as.factor(callout_wardid == 1)                                 10490
## cut2(PROPFULL_BEDS, c(0.9, 1))                                 10430
## MED_SERVICE                                                    10429
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10430
##                                                                   LRT
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.10
## cut2(oasis, g = 3)                                              35.94
## cut2(age, g = 3)                                                 6.92
## female                                                           0.51
## request_tele                                                     0.20
## request_resp                                                     3.16
## request_mrsa                                                     2.08
## request_vre                                                     30.08
## request_cdiff                                                   10.58
## cut2(elixhauser_hospital, g = 3)                               132.19
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     348.08
## as.factor(callout_month)                                        13.86
## as.factor(callout_year)                                         29.00
## as.factor(callout_dayofweek)                                     4.63
## as.factor(callout_wardid == 1)                                  61.09
## cut2(PROPFULL_BEDS, c(0.9, 1))                                   2.86
## MED_SERVICE                                                      0.03
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   4.50
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.749877
## cut2(oasis, g = 3)                                             1.567e-08
## cut2(age, g = 3)                                                0.031357
## female                                                          0.473340
## request_tele                                                    0.656905
## request_resp                                                    0.075464
## request_mrsa                                                    0.148811
## request_vre                                                    4.138e-08
## request_cdiff                                                   0.001146
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.241032
## as.factor(callout_year)                                        6.094e-05
## as.factor(callout_dayofweek)                                    0.592587
## as.factor(callout_wardid == 1)                                 5.440e-15
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  0.239400
## MED_SERVICE                                                     0.868893
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.212105
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               *  
## female                                                            
## request_tele                                                      
## request_resp                                                   .  
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                      
## as.factor(callout_wardid == 1)                                 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                                    
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "MED_SERVICE"
## [1] 0.8688934
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10336
## cut2(oasis, g = 3)                                              2    10371
## cut2(age, g = 3)                                                2    10342
## female                                                          1    10336
## request_tele                                                    1    10336
## request_resp                                                    1    10339
## request_mrsa                                                    1    10338
## request_vre                                                     1    10366
## request_cdiff                                                   1    10346
## cut2(elixhauser_hospital, g = 3)                                2    10468
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10685
## as.factor(callout_month)                                       11    10349
## as.factor(callout_year)                                         6    10364
## as.factor(callout_dayofweek)                                    6    10340
## as.factor(callout_wardid == 1)                                  1    10400
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  2    10338
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10340
##                                                                  AIC
## <none>                                                         10429
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10428
## cut2(oasis, g = 3)                                             10461
## cut2(age, g = 3)                                               10432
## female                                                         10428
## request_tele                                                   10428
## request_resp                                                   10431
## request_mrsa                                                   10430
## request_vre                                                    10458
## request_cdiff                                                  10438
## cut2(elixhauser_hospital, g = 3)                               10558
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10771
## as.factor(callout_month)                                       10421
## as.factor(callout_year)                                        10446
## as.factor(callout_dayofweek)                                   10422
## as.factor(callout_wardid == 1)                                 10492
## cut2(PROPFULL_BEDS, c(0.9, 1))                                 10428
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10428
##                                                                   LRT
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.10
## cut2(oasis, g = 3)                                              35.99
## cut2(age, g = 3)                                                 6.90
## female                                                           0.51
## request_tele                                                     0.20
## request_resp                                                     3.15
## request_mrsa                                                     2.07
## request_vre                                                     30.06
## request_cdiff                                                   10.56
## cut2(elixhauser_hospital, g = 3)                               132.40
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     349.56
## as.factor(callout_month)                                        13.84
## as.factor(callout_year)                                         28.97
## as.factor(callout_dayofweek)                                     4.61
## as.factor(callout_wardid == 1)                                  65.09
## cut2(PROPFULL_BEDS, c(0.9, 1))                                   2.86
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   4.50
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.751246
## cut2(oasis, g = 3)                                             1.527e-08
## cut2(age, g = 3)                                                0.031673
## female                                                          0.474119
## request_tele                                                    0.654468
## request_resp                                                    0.076015
## request_mrsa                                                    0.150224
## request_vre                                                    4.194e-08
## request_cdiff                                                   0.001154
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.241790
## as.factor(callout_year)                                        6.166e-05
## as.factor(callout_dayofweek)                                    0.594267
## as.factor(callout_wardid == 1)                                 7.167e-16
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  0.239751
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.212215
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               *  
## female                                                            
## request_tele                                                      
## request_resp                                                   .  
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                      
## as.factor(callout_wardid == 1)                                 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                                    
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_tele"
## [1] 0.6544675
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10336
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10336
## cut2(oasis, g = 3)                                              2    10372
## cut2(age, g = 3)                                                2    10342
## female                                                          1    10336
## request_resp                                                    1    10339
## request_mrsa                                                    1    10338
## request_vre                                                     1    10366
## request_cdiff                                                   1    10346
## cut2(elixhauser_hospital, g = 3)                                2    10468
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10685
## as.factor(callout_month)                                       11    10349
## as.factor(callout_year)                                         6    10364
## as.factor(callout_dayofweek)                                    6    10340
## as.factor(callout_wardid == 1)                                  1    10401
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  2    10338
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10340
##                                                                  AIC
## <none>                                                         10428
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10426
## cut2(oasis, g = 3)                                             10460
## cut2(age, g = 3)                                               10430
## female                                                         10426
## request_resp                                                   10429
## request_mrsa                                                   10428
## request_vre                                                    10456
## request_cdiff                                                  10436
## cut2(elixhauser_hospital, g = 3)                               10556
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10769
## as.factor(callout_month)                                       10419
## as.factor(callout_year)                                        10444
## as.factor(callout_dayofweek)                                   10420
## as.factor(callout_wardid == 1)                                 10491
## cut2(PROPFULL_BEDS, c(0.9, 1))                                 10426
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10426
##                                                                   LRT
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.10
## cut2(oasis, g = 3)                                              35.88
## cut2(age, g = 3)                                                 6.78
## female                                                           0.52
## request_resp                                                     3.12
## request_mrsa                                                     2.06
## request_vre                                                     29.99
## request_cdiff                                                   10.54
## cut2(elixhauser_hospital, g = 3)                               132.86
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     349.59
## as.factor(callout_month)                                        13.80
## as.factor(callout_year)                                         28.78
## as.factor(callout_dayofweek)                                     4.58
## as.factor(callout_wardid == 1)                                  65.16
## cut2(PROPFULL_BEDS, c(0.9, 1))                                   2.85
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   4.54
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.746469
## cut2(oasis, g = 3)                                             1.616e-08
## cut2(age, g = 3)                                                0.033697
## female                                                          0.470529
## request_resp                                                    0.077450
## request_mrsa                                                    0.151243
## request_vre                                                    4.338e-08
## request_cdiff                                                   0.001169
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.244042
## as.factor(callout_year)                                        6.702e-05
## as.factor(callout_dayofweek)                                    0.598040
## as.factor(callout_wardid == 1)                                 6.901e-16
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  0.239943
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.208739
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               *  
## female                                                            
## request_resp                                                   .  
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                      
## as.factor(callout_wardid == 1)                                 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                                    
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_dayofweek)"
## [1] 0.5980398
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10340
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10340
## cut2(oasis, g = 3)                                              2    10376
## cut2(age, g = 3)                                                2    10347
## female                                                          1    10341
## request_resp                                                    1    10343
## request_mrsa                                                    1    10342
## request_vre                                                     1    10371
## request_cdiff                                                   1    10350
## cut2(elixhauser_hospital, g = 3)                                2    10473
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10692
## as.factor(callout_month)                                       11    10353
## as.factor(callout_year)                                         6    10369
## as.factor(callout_wardid == 1)                                  1    10406
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  2    10342
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10345
##                                                                  AIC
## <none>                                                         10420
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10418
## cut2(oasis, g = 3)                                             10452
## cut2(age, g = 3)                                               10423
## female                                                         10419
## request_resp                                                   10421
## request_mrsa                                                   10420
## request_vre                                                    10449
## request_cdiff                                                  10428
## cut2(elixhauser_hospital, g = 3)                               10549
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10764
## as.factor(callout_month)                                       10411
## as.factor(callout_year)                                        10437
## as.factor(callout_wardid == 1)                                 10484
## cut2(PROPFULL_BEDS, c(0.9, 1))                                 10418
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10419
##                                                                   LRT
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.04
## cut2(oasis, g = 3)                                              36.19
## cut2(age, g = 3)                                                 6.84
## female                                                           0.55
## request_resp                                                     3.09
## request_mrsa                                                     2.08
## request_vre                                                     30.50
## request_cdiff                                                   10.29
## cut2(elixhauser_hospital, g = 3)                               133.04
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     351.60
## as.factor(callout_month)                                        13.09
## as.factor(callout_year)                                         28.62
## as.factor(callout_wardid == 1)                                  66.27
## cut2(PROPFULL_BEDS, c(0.9, 1))                                   1.37
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   4.80
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.849001
## cut2(oasis, g = 3)                                             1.387e-08
## cut2(age, g = 3)                                                0.032693
## female                                                          0.457920
## request_resp                                                    0.078601
## request_mrsa                                                    0.149247
## request_vre                                                    3.338e-08
## request_cdiff                                                   0.001338
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.287674
## as.factor(callout_year)                                        7.166e-05
## as.factor(callout_wardid == 1)                                 3.937e-16
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  0.503443
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.186647
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               *  
## female                                                            
## request_resp                                                   .  
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_wardid == 1)                                 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                                    
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(PROPFULL_BEDS, c(0.9, 1))"
## [1] 0.5034429
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10342
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10342
## cut2(oasis, g = 3)                                              2    10378
## cut2(age, g = 3)                                                2    10348
## female                                                          1    10342
## request_resp                                                    1    10345
## request_mrsa                                                    1    10344
## request_vre                                                     1    10372
## request_cdiff                                                   1    10352
## cut2(elixhauser_hospital, g = 3)                                2    10474
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10694
## as.factor(callout_month)                                       11    10354
## as.factor(callout_year)                                         6    10370
## as.factor(callout_wardid == 1)                                  1    10408
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10346
##                                                                  AIC
## <none>                                                         10418
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10416
## cut2(oasis, g = 3)                                             10450
## cut2(age, g = 3)                                               10420
## female                                                         10416
## request_resp                                                   10419
## request_mrsa                                                   10418
## request_vre                                                    10446
## request_cdiff                                                  10426
## cut2(elixhauser_hospital, g = 3)                               10546
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10762
## as.factor(callout_month)                                       10408
## as.factor(callout_year)                                        10434
## as.factor(callout_wardid == 1)                                 10482
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10416
##                                                                   LRT
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.00
## cut2(oasis, g = 3)                                              36.40
## cut2(age, g = 3)                                                 6.78
## female                                                           0.56
## request_resp                                                     3.04
## request_mrsa                                                     2.06
## request_vre                                                     30.88
## request_cdiff                                                   10.30
## cut2(elixhauser_hospital, g = 3)                               132.47
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     352.08
## as.factor(callout_month)                                        12.86
## as.factor(callout_year)                                         28.60
## as.factor(callout_wardid == 1)                                  65.94
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   4.85
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.984937
## cut2(oasis, g = 3)                                             1.248e-08
## cut2(age, g = 3)                                                0.033681
## female                                                          0.453775
## request_resp                                                    0.080990
## request_mrsa                                                    0.151565
## request_vre                                                    2.746e-08
## request_cdiff                                                   0.001333
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.302880
## as.factor(callout_year)                                        7.252e-05
## as.factor(callout_wardid == 1)                                 4.649e-16
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.183393
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               *  
## female                                                            
## request_resp                                                   .  
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_wardid == 1)                                 ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "female"
## [1] 0.4537755
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10342
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10342
## cut2(oasis, g = 3)                                              2    10378
## cut2(age, g = 3)                                                2    10349
## request_resp                                                    1    10345
## request_mrsa                                                    1    10344
## request_vre                                                     1    10373
## request_cdiff                                                   1    10352
## cut2(elixhauser_hospital, g = 3)                                2    10477
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10694
## as.factor(callout_month)                                       11    10355
## as.factor(callout_year)                                         6    10371
## as.factor(callout_wardid == 1)                                  1    10408
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10347
##                                                                  AIC
## <none>                                                         10416
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10414
## cut2(oasis, g = 3)                                             10448
## cut2(age, g = 3)                                               10419
## request_resp                                                   10417
## request_mrsa                                                   10416
## request_vre                                                    10445
## request_cdiff                                                  10424
## cut2(elixhauser_hospital, g = 3)                               10547
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10760
## as.factor(callout_month)                                       10407
## as.factor(callout_year)                                        10433
## as.factor(callout_wardid == 1)                                 10480
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10415
##                                                                   LRT
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.00
## cut2(oasis, g = 3)                                              36.18
## cut2(age, g = 3)                                                 7.05
## request_resp                                                     3.07
## request_mrsa                                                     2.10
## request_vre                                                     30.67
## request_cdiff                                                   10.24
## cut2(elixhauser_hospital, g = 3)                               134.84
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     351.73
## as.factor(callout_month)                                        12.76
## as.factor(callout_year)                                         28.44
## as.factor(callout_wardid == 1)                                  65.86
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   4.89
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.968514
## cut2(oasis, g = 3)                                             1.391e-08
## cut2(age, g = 3)                                                0.029411
## request_resp                                                    0.079574
## request_mrsa                                                    0.147255
## request_vre                                                    3.053e-08
## request_cdiff                                                   0.001371
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.308979
## as.factor(callout_year)                                        7.764e-05
## as.factor(callout_wardid == 1)                                 4.837e-16
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.180350
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               *  
## request_resp                                                   .  
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_wardid == 1)                                 ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_month)"
## [1] 0.308979
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid == 
##     1) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                               10355
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1    10355
## cut2(oasis, g = 3)                                              2    10391
## cut2(age, g = 3)                                                2    10362
## request_resp                                                    1    10358
## request_mrsa                                                    1    10357
## request_vre                                                     1    10386
## request_cdiff                                                   1    10366
## cut2(elixhauser_hospital, g = 3)                                2    10489
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4    10704
## as.factor(callout_year)                                         6    10381
## as.factor(callout_wardid == 1)                                  1    10421
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3    10360
##                                                                  AIC
## <none>                                                         10407
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    10405
## cut2(oasis, g = 3)                                             10439
## cut2(age, g = 3)                                               10410
## request_resp                                                   10408
## request_mrsa                                                   10407
## request_vre                                                    10436
## request_cdiff                                                  10416
## cut2(elixhauser_hospital, g = 3)                               10537
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     10748
## as.factor(callout_year)                                        10421
## as.factor(callout_wardid == 1)                                 10471
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10406
##                                                                   LRT
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.00
## cut2(oasis, g = 3)                                              35.84
## cut2(age, g = 3)                                                 6.90
## request_resp                                                     2.70
## request_mrsa                                                     2.16
## request_vre                                                     30.60
## request_cdiff                                                   10.72
## cut2(elixhauser_hospital, g = 3)                               133.81
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     348.85
## as.factor(callout_year)                                         25.90
## as.factor(callout_wardid == 1)                                  66.12
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   4.71
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.9538684
## cut2(oasis, g = 3)                                             1.646e-08
## cut2(age, g = 3)                                               0.0318001
## request_resp                                                   0.1000410
## request_mrsa                                                   0.1413777
## request_vre                                                    3.165e-08
## request_cdiff                                                  0.0010572
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_year)                                        0.0002324
## as.factor(callout_wardid == 1)                                 4.242e-16
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1942305
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               *  
## request_resp                                                      
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_year)                                        ***
## as.factor(callout_wardid == 1)                                 ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "relevel(cut2(hourofcallout2, c(7, 12, 19)), \"[ 7.000,12.000)\")"
## [1] 0.1942305
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     request_resp + request_mrsa + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid == 
##     1)
##                                                             Df Deviance
## <none>                                                            10360
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1    10360
## cut2(oasis, g = 3)                                           2    10396
## cut2(age, g = 3)                                             2    10367
## request_resp                                                 1    10362
## request_mrsa                                                 1    10362
## request_vre                                                  1    10390
## request_cdiff                                                1    10370
## cut2(elixhauser_hospital, g = 3)                             2    10494
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4    10710
## as.factor(callout_year)                                      6    10384
## as.factor(callout_wardid == 1)                               1    10426
##                                                               AIC    LRT
## <none>                                                      10406       
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10404   0.01
## cut2(oasis, g = 3)                                          10438  36.04
## cut2(age, g = 3)                                            10409   7.02
## request_resp                                                10406   2.57
## request_mrsa                                                10406   2.16
## request_vre                                                 10434  30.78
## request_cdiff                                               10414  10.60
## cut2(elixhauser_hospital, g = 3)                            10536 134.19
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  10748 350.35
## as.factor(callout_year)                                     10418  24.90
## as.factor(callout_wardid == 1)                              10470  65.97
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9128998    
## cut2(oasis, g = 3)                                          1.489e-08 ***
## cut2(age, g = 3)                                            0.0298308 *  
## request_resp                                                0.1089144    
## request_mrsa                                                0.1416814    
## request_vre                                                 2.884e-08 ***
## request_cdiff                                               0.0011290 ** 
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_year)                                     0.0003568 ***
## as.factor(callout_wardid == 1)                              4.580e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_mrsa"
## [1] 0.1416814
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     request_resp + request_vre + request_cdiff + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1)
##                                                             Df Deviance
## <none>                                                            10362
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1    10362
## cut2(oasis, g = 3)                                           2    10398
## cut2(age, g = 3)                                             2    10369
## request_resp                                                 1    10364
## request_vre                                                  1    10396
## request_cdiff                                                1    10372
## cut2(elixhauser_hospital, g = 3)                             2    10496
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4    10716
## as.factor(callout_year)                                      6    10386
## as.factor(callout_wardid == 1)                               1    10426
##                                                               AIC    LRT
## <none>                                                      10406       
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10404   0.04
## cut2(oasis, g = 3)                                          10438  36.41
## cut2(age, g = 3)                                            10409   6.97
## request_resp                                                10406   2.66
## request_vre                                                 10438  34.36
## request_cdiff                                               10414  10.62
## cut2(elixhauser_hospital, g = 3)                            10536 134.14
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  10752 353.93
## as.factor(callout_year)                                     10418  24.49
## as.factor(callout_wardid == 1)                              10468  64.69
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8495831    
## cut2(oasis, g = 3)                                          1.243e-08 ***
## cut2(age, g = 3)                                            0.0306513 *  
## request_resp                                                0.1025802    
## request_vre                                                 4.573e-09 ***
## request_cdiff                                               0.0011206 ** 
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_year)                                     0.0004238 ***
## as.factor(callout_wardid == 1)                              8.749e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_resp"
## [1] 0.1025802
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1)
##                                                             Df Deviance
## <none>                                                            10364
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1    10364
## cut2(oasis, g = 3)                                           2    10401
## cut2(age, g = 3)                                             2    10372
## request_vre                                                  1    10399
## request_cdiff                                                1    10375
## cut2(elixhauser_hospital, g = 3)                             2    10499
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4    10716
## as.factor(callout_year)                                      6    10388
## as.factor(callout_wardid == 1)                               1    10429
##                                                               AIC    LRT
## <none>                                                      10406       
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10404   0.04
## cut2(oasis, g = 3)                                          10439  36.47
## cut2(age, g = 3)                                            10410   7.19
## request_vre                                                 10439  34.23
## request_cdiff                                               10415  10.72
## cut2(elixhauser_hospital, g = 3)                            10537 134.28
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  10750 351.96
## as.factor(callout_year)                                     10418  23.99
## as.factor(callout_wardid == 1)                              10469  64.66
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8437574    
## cut2(oasis, g = 3)                                          1.201e-08 ***
## cut2(age, g = 3)                                            0.0274167 *  
## request_vre                                                 4.908e-09 ***
## request_cdiff                                               0.0010587 ** 
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_year)                                     0.0005246 ***
## as.factor(callout_wardid == 1)                              8.898e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(age, g = 3)"
## [1] 0.02741666
    I(HOSP_FREE_DAYS > 21)
    Odds Ratio CI p
(Intercept)   3.69 2.76 – 4.96 <.001
I(cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]”)   0.98 0.84 – 1.15 .844
cut2(oasis, g = 3)
[27,34)   0.82 0.73 – 0.92 .001
[34,64]   0.68 0.59 – 0.77 <.001
cut2(age, g = 3)
[56.1,73.8)   0.97 0.86 – 1.09 .631
[73.8,91.4]   1.14 1.00 – 1.29 .050
request_vre   0.55 0.45 – 0.67 <.001
request_cdiff   0.71 0.58 – 0.87 <.001
cut2(elixhauser_hospital, g = 3)
[ 1, 7)   0.68 0.61 – 0.77 <.001
[ 7,31]   0.50 0.44 – 0.56 <.001
cut2(los_pre_callout_days, c(1, 3, 7, 28))
[ 1.000, 3.000)   0.84 0.74 – 0.96 .008
[ 3.000, 7.000)   0.53 0.46 – 0.61 <.001
[ 7.000, 28.000)   0.27 0.23 – 0.32 <.001
[ 28.000,130.762]   0.12 0.06 – 0.22 <.001
as.factor(callout_year)
2006   1.01 0.77 – 1.32 .938
2007   1.16 0.89 – 1.50 .262
2008   1.37 1.05 – 1.77 .018
2009   1.40 1.07 – 1.81 .012
2010   1.26 0.97 – 1.63 .077
2011   1.44 1.11 – 1.86 .006
as.factor(callout_wardid == 1) (TRUE)   1.69 1.49 – 1.92 <.001
Observations   9673
##                                                                      
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.984
## cut2(oasis, g = 3)[27,34)                                       0.822
## cut2(oasis, g = 3)[34,64]                                       0.675
## cut2(age, g = 3)[56.1,73.8)                                     0.971
## cut2(age, g = 3)[73.8,91.4]                                     1.136
## request_vre                                                     0.549
## request_cdiff                                                   0.711
## cut2(elixhauser_hospital, g = 3)[  1, 7)                        0.685
## cut2(elixhauser_hospital, g = 3)[  7,31]                        0.496
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)     0.843
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)     0.532
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)     0.270
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     0.120
## as.factor(callout_year)2006                                     1.011
## as.factor(callout_year)2007                                     1.161
## as.factor(callout_year)2008                                     1.369
## as.factor(callout_year)2009                                     1.396
## as.factor(callout_year)2010                                     1.261
## as.factor(callout_year)2011                                     1.442
## as.factor(callout_wardid == 1)TRUE                              1.691
##                                                                 2.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.843
## cut2(oasis, g = 3)[27,34)                                       0.731
## cut2(oasis, g = 3)[34,64]                                       0.594
## cut2(age, g = 3)[56.1,73.8)                                     0.862
## cut2(age, g = 3)[73.8,91.4]                                     1.000
## request_vre                                                     0.451
## request_cdiff                                                   0.582
## cut2(elixhauser_hospital, g = 3)[  1, 7)                        0.606
## cut2(elixhauser_hospital, g = 3)[  7,31]                        0.440
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)     0.744
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)     0.461
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)     0.230
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     0.062
## as.factor(callout_year)2006                                     0.771
## as.factor(callout_year)2007                                     0.893
## as.factor(callout_year)2008                                     1.054
## as.factor(callout_year)2009                                     1.074
## as.factor(callout_year)2010                                     0.972
## as.factor(callout_year)2011                                     1.110
## as.factor(callout_wardid == 1)TRUE                              1.490
##                                                                 97.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE  1.152
## cut2(oasis, g = 3)[27,34)                                        0.925
## cut2(oasis, g = 3)[34,64]                                        0.767
## cut2(age, g = 3)[56.1,73.8)                                      1.094
## cut2(age, g = 3)[73.8,91.4]                                      1.290
## request_vre                                                      0.670
## request_cdiff                                                    0.871
## cut2(elixhauser_hospital, g = 3)[  1, 7)                         0.774
## cut2(elixhauser_hospital, g = 3)[  7,31]                         0.559
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)      0.956
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)      0.613
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)      0.316
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]      0.221
## as.factor(callout_year)2006                                      1.319
## as.factor(callout_year)2007                                      1.503
## as.factor(callout_year)2008                                      1.771
## as.factor(callout_year)2009                                      1.807
## as.factor(callout_year)2010                                      1.627
## as.factor(callout_year)2011                                      1.864
## as.factor(callout_wardid == 1)TRUE                               1.917
##                                                                      
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.844
## cut2(oasis, g = 3)[27,34)                                       0.001
## cut2(oasis, g = 3)[34,64]                                       0.000
## cut2(age, g = 3)[56.1,73.8)                                     0.631
## cut2(age, g = 3)[73.8,91.4]                                     0.050
## request_vre                                                     0.000
## request_cdiff                                                   0.001
## cut2(elixhauser_hospital, g = 3)[  1, 7)                        0.000
## cut2(elixhauser_hospital, g = 3)[  7,31]                        0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)     0.008
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)     0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)     0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     0.000
## as.factor(callout_year)2006                                     0.938
## as.factor(callout_year)2007                                     0.262
## as.factor(callout_year)2008                                     0.018
## as.factor(callout_year)2009                                     0.012
## as.factor(callout_year)2010                                     0.077
## as.factor(callout_year)2011                                     0.006
## as.factor(callout_wardid == 1)TRUE                              0.000

Answer 4: After adjusting for potential confounders, there is no statistically significant evidence that a long delay produces a better HFD outcome (>21 days).

Question 5: Do people who have long discharge delays (>24 hours) have a “bad outcome” defined as a long post discharge LOS (>3 week) or death?

## 
## FALSE  TRUE 
##  8839   834
## 
## FALSE  TRUE 
##  8662  1011
##        
##         FALSE TRUE
##   FALSE  7934  905
##   TRUE    728  106
##        
##              FALSE       TRUE
##   FALSE 0.91595474 0.89515331
##   TRUE  0.08404526 0.10484669
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  with(d, table(HOSP_FREE_DAYS <= 7, cut2(DISCHARGEDELAY_HOURS,     c(24)) == "[ 24.000,129.566]"))
## X-squared = 4.7117, df = 1, p-value = 0.02996
Patient Characteristics Overall
variable_name level FALSE TRUE p test
n 8839 834
micu (%) 1 8839 (100.0) 834 (100.0) NA
age (mean (sd)) 63.51 (18.07) 65.50 (16.43) 0.002
callout_month (%) 1 701 ( 7.9) 73 ( 8.8) 0.749
2 677 ( 7.7) 61 ( 7.3)
3 656 ( 7.4) 77 ( 9.2)
4 660 ( 7.5) 66 ( 7.9)
5 726 ( 8.2) 68 ( 8.2)
6 687 ( 7.8) 58 ( 7.0)
7 714 ( 8.1) 65 ( 7.8)
8 798 ( 9.0) 79 ( 9.5)
9 776 ( 8.8) 75 ( 9.0)
10 843 ( 9.5) 71 ( 8.5)
11 762 ( 8.6) 61 ( 7.3)
12 839 ( 9.5) 80 ( 9.6)
female (%) 0 4503 ( 50.9) 453 ( 54.3) 0.068
1 4336 ( 49.1) 381 ( 45.7)
request_tele (%) 0 5741 ( 65.0) 577 ( 69.2) 0.016
1 3098 ( 35.0) 257 ( 30.8)
request_resp (%) 0 8688 ( 98.3) 826 ( 99.0) 0.138
1 151 ( 1.7) 8 ( 1.0)
request_cdiff (%) 0 8430 ( 95.4) 760 ( 91.1) <0.001
1 409 ( 4.6) 74 ( 8.9)
request_mrsa (%) 0 7695 ( 87.1) 710 ( 85.1) 0.128
1 1144 ( 12.9) 124 ( 14.9)
request_vre (%) 0 8428 ( 95.4) 754 ( 90.4) <0.001
1 411 ( 4.6) 80 ( 9.6)
oasis (mean (sd)) 29.54 (7.13) 32.97 (8.22) <0.001
elixhauser_hospital (mean (sd)) 3.18 (7.18) 7.40 (7.22) <0.001
ethnicity (%) White 6295 ( 71.2) 615 ( 73.7) 0.025
African American/Black 1392 ( 15.7) 102 ( 12.2)
Other 1152 ( 13.0) 117 ( 14.0)
MED_SERVICE (%) FALSE 631 ( 7.1) 61 ( 7.3) 0.906
TRUE 8208 ( 92.9) 773 ( 92.7)
HOSP_FREE_DAYS (mean (sd)) 23.29 (4.04) 0.62 (1.61) <0.001
callout_dayofweek (%) friday 1321 ( 14.9) 121 ( 14.5) 0.316
monday 1176 ( 13.3) 124 ( 14.9)
saturday 1196 ( 13.5) 96 ( 11.5)
sunday 1161 ( 13.1) 106 ( 12.7)
thursday 1284 ( 14.5) 128 ( 15.3)
tuesday 1285 ( 14.5) 137 ( 16.4)
wednesday 1416 ( 16.0) 122 ( 14.6)
CALLOUT_DURING_NIGHT (%) FALSE 8783 ( 99.4) 824 ( 98.8) 0.094
TRUE 56 ( 0.6) 10 ( 1.2)
CALLOUT_DURING_ROUNDS (%) FALSE 3456 ( 39.1) 360 ( 43.2) 0.024
TRUE 5383 ( 60.9) 474 ( 56.8)
DISCHARGEDELAY_HOURS (mean (sd)) 10.30 (10.14) 11.11 (11.60) 0.029
hourofcallout2 (median [IQR]) 11.38 [10.10, 13.13] 11.60 [10.28, 13.52] 0.012 nonnorm
PROPFULL_BEDS (mean (sd)) 0.91 (0.09) 0.91 (0.09) 0.762
postcalldaycat2 (%) 0 7153 ( 80.9) 647 ( 77.6) 0.022
[1,5] 1686 ( 19.1) 187 ( 22.4)
los_preicu_days (median [IQR]) 0.00 [0.00, 0.11] 0.00 [0.00, 1.71] <0.001 nonnorm
los_post_callout_days (median [IQR]) 4.06 [2.25, 6.34] 16.00 [4.89, 28.19] <0.001 nonnorm
los_post_icu_days (median [IQR]) 3.65 [1.92, 6.00] 15.75 [4.46, 27.90] <0.001 nonnorm
los_pre_callout_days (median [IQR]) 1.72 [0.91, 3.46] 3.55 [1.57, 7.91] <0.001 nonnorm
callout_year (%) 2005 335 ( 3.8) 41 ( 4.9) 0.002
2006 953 ( 10.8) 117 ( 14.0)
2007 1282 ( 14.5) 135 ( 16.2)
2008 1509 ( 17.1) 129 ( 15.5)
2009 1530 ( 17.3) 138 ( 16.5)
2010 1583 ( 17.9) 156 ( 18.7)
2011 1647 ( 18.6) 118 ( 14.1)
hospitaldeath (mean (sd)) 0.00 (0.00) 0.64 (0.48) <0.001
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5035.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5035.7
## cut2(oasis, g = 3)                                              2   5114.3
## cut2(age, g = 3)                                                2   5040.7
## female                                                          1   5037.7
## request_tele                                                    1   5043.2
## request_resp                                                    1   5036.5
## request_mrsa                                                    1   5035.7
## request_vre                                                     1   5050.6
## request_cdiff                                                   1   5044.4
## cut2(elixhauser_hospital, g = 3)                                2   5150.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5198.9
## as.factor(callout_month)                                       11   5046.9
## as.factor(callout_year)                                         6   5058.4
## as.factor(callout_dayofweek)                                    6   5040.4
## MED_SERVICE                                                     1   5037.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5042.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5036.9
##                                                                   AIC
## <none>                                                         5135.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5133.7
## cut2(oasis, g = 3)                                             5210.3
## cut2(age, g = 3)                                               5136.7
## female                                                         5135.7
## request_tele                                                   5141.2
## request_resp                                                   5134.5
## request_mrsa                                                   5133.7
## request_vre                                                    5148.6
## request_cdiff                                                  5142.4
## cut2(elixhauser_hospital, g = 3)                               5246.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5290.9
## as.factor(callout_month)                                       5124.9
## as.factor(callout_year)                                        5146.4
## as.factor(callout_dayofweek)                                   5128.4
## MED_SERVICE                                                    5135.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5136.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5132.9
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.031
## cut2(oasis, g = 3)                                              78.704
## cut2(age, g = 3)                                                 5.083
## female                                                           2.109
## request_tele                                                     7.538
## request_resp                                                     0.869
## request_mrsa                                                     0.062
## request_vre                                                     14.966
## request_cdiff                                                    8.748
## cut2(elixhauser_hospital, g = 3)                               114.761
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     163.314
## as.factor(callout_month)                                        11.314
## as.factor(callout_year)                                         22.785
## as.factor(callout_dayofweek)                                     4.771
## MED_SERVICE                                                      1.462
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.021
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    1.277
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.8607004
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.0787439
## female                                                         0.1464668
## request_tele                                                   0.0060405
## request_resp                                                   0.3512004
## request_mrsa                                                   0.8040967
## request_vre                                                    0.0001094
## request_cdiff                                                  0.0030987
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.4173211
## as.factor(callout_year)                                        0.0008717
## as.factor(callout_dayofweek)                                   0.5734966
## MED_SERVICE                                                    0.2266808
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0712288
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.5280732
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_resp                                                      
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                      
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + request_tele + 
##     request_vre + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_wardid == 1)
##                                                             Df Deviance
## <none>                                                           5099.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1   5101.1
## cut2(oasis, g = 3)                                           2   5178.0
## request_tele                                                 1   5110.0
## request_vre                                                  1   5115.8
## cut2(elixhauser_hospital, g = 3)                             2   5221.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4   5275.2
## as.factor(callout_wardid == 1)                               1   5145.5
##                                                                AIC     LRT
## <none>                                                      5125.6        
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5125.1   1.569
## cut2(oasis, g = 3)                                          5200.0  78.442
## request_tele                                                5134.0  10.409
## request_vre                                                 5139.8  16.218
## cut2(elixhauser_hospital, g = 3)                            5243.5 121.963
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  5293.2 175.693
## as.factor(callout_wardid == 1)                              5169.5  45.983
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  0.210416    
## cut2(oasis, g = 3)                                          < 2.2e-16 ***
## request_tele                                                 0.001254 ** 
## request_vre                                                 5.644e-05 ***
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_wardid == 1)                              1.193e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5056.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5056.4
## cut2(oasis, g = 3)                                              2   5137.9
## cut2(age, g = 3)                                                2   5061.7
## female                                                          1   5058.6
## request_tele                                                    1   5063.8
## request_vre                                                     1   5072.5
## request_cdiff                                                   1   5065.4
## cut2(elixhauser_hospital, g = 3)                                2   5173.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5219.4
## as.factor(callout_year)                                         6   5078.2
## as.factor(callout_wardid == 1)                                  1   5105.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5063.6
##                                                                   AIC
## <none>                                                         5108.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5106.4
## cut2(oasis, g = 3)                                             5185.9
## cut2(age, g = 3)                                               5109.7
## female                                                         5108.6
## request_tele                                                   5113.8
## request_vre                                                    5122.5
## request_cdiff                                                  5115.4
## cut2(elixhauser_hospital, g = 3)                               5221.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5263.4
## as.factor(callout_year)                                        5118.2
## as.factor(callout_wardid == 1)                                 5155.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5109.6
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.002
## cut2(oasis, g = 3)                                              81.467
## cut2(age, g = 3)                                                 5.315
## female                                                           2.190
## request_tele                                                     7.363
## request_vre                                                     16.138
## request_cdiff                                                    8.986
## cut2(elixhauser_hospital, g = 3)                               117.126
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     162.980
## as.factor(callout_year)                                         21.842
## as.factor(callout_wardid == 1)                                  48.977
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.227
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.960419
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                                0.070133
## female                                                          0.138896
## request_tele                                                    0.006656
## request_vre                                                     5.89e-05
## request_cdiff                                                   0.002721
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_year)                                         0.001293
## as.factor(callout_wardid == 1)                                  2.59e-12
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.065003
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_year)                                        ** 
## as.factor(callout_wardid == 1)                                 ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## glm(formula = I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, 
##     c(24)) == "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, 
##     g = 3) + female + request_tele + request_resp + request_mrsa + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9, 
##         1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 
##     12, 19)), "[ 7.000,12.000)"), family = "binomial", data = d)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8597  -0.4415  -0.3211  -0.2346   2.9127  
## 
## Coefficients:
##                                                                                 Estimate
## (Intercept)                                                                    -2.796181
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                 0.021616
## cut2(oasis, g = 3)[27,34)                                                       0.321378
## cut2(oasis, g = 3)[34,64]                                                       0.881226
## cut2(age, g = 3)[56.1,73.8)                                                    -0.074754
## cut2(age, g = 3)[73.8,91.4]                                                    -0.226033
## female                                                                         -0.111347
## request_tele                                                                   -0.224724
## request_resp                                                                   -0.334228
## request_mrsa                                                                    0.027336
## request_vre                                                                     0.567030
## request_cdiff                                                                   0.431446
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                        0.520595
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        1.050986
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                     0.002218
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                     0.591122
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                     1.076589
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                     2.371992
## as.factor(callout_month)2                                                      -0.151334
## as.factor(callout_month)3                                                       0.036299
## as.factor(callout_month)4                                                      -0.070657
## as.factor(callout_month)5                                                      -0.164206
## as.factor(callout_month)6                                                      -0.234734
## as.factor(callout_month)7                                                      -0.179483
## as.factor(callout_month)8                                                      -0.070305
## as.factor(callout_month)9                                                      -0.184683
## as.factor(callout_month)10                                                     -0.313408
## as.factor(callout_month)11                                                     -0.465702
## as.factor(callout_month)12                                                     -0.163809
## as.factor(callout_year)2006                                                    -0.116078
## as.factor(callout_year)2007                                                    -0.293361
## as.factor(callout_year)2008                                                    -0.447500
## as.factor(callout_year)2009                                                    -0.524895
## as.factor(callout_year)2010                                                    -0.378814
## as.factor(callout_year)2011                                                    -0.697084
## as.factor(callout_dayofweek)monday                                              0.128210
## as.factor(callout_dayofweek)saturday                                           -0.019462
## as.factor(callout_dayofweek)sunday                                              0.029041
## as.factor(callout_dayofweek)thursday                                            0.055857
## as.factor(callout_dayofweek)tuesday                                             0.189420
## as.factor(callout_dayofweek)wednesday                                          -0.069679
## as.factor(callout_wardid == 1)TRUE                                             -0.573508
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                     0.168689
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                     0.052471
## MED_SERVICETRUE                                                                 0.185641
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)   0.849213
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)   0.096323
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]   0.357861
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -0.209857
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.219414
##                                                                                Std. Error
## (Intercept)                                                                      0.341984
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                  0.123002
## cut2(oasis, g = 3)[27,34)                                                        0.103669
## cut2(oasis, g = 3)[34,64]                                                        0.104732
## cut2(age, g = 3)[56.1,73.8)                                                      0.097007
## cut2(age, g = 3)[73.8,91.4]                                                      0.103509
## female                                                                           0.076750
## request_tele                                                                     0.082644
## request_resp                                                                     0.374775
## request_mrsa                                                                     0.109974
## request_vre                                                                      0.140655
## request_cdiff                                                                    0.140900
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                         0.112033
## cut2(elixhauser_hospital, g = 3)[  7,31]                                         0.104206
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                      0.110923
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                      0.116433
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                      0.121483
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                      0.303472
## as.factor(callout_month)2                                                        0.190685
## as.factor(callout_month)3                                                        0.182247
## as.factor(callout_month)4                                                        0.187913
## as.factor(callout_month)5                                                        0.185860
## as.factor(callout_month)6                                                        0.193114
## as.factor(callout_month)7                                                        0.187621
## as.factor(callout_month)8                                                        0.180723
## as.factor(callout_month)9                                                        0.182913
## as.factor(callout_month)10                                                       0.185124
## as.factor(callout_month)11                                                       0.192103
## as.factor(callout_month)12                                                       0.181808
## as.factor(callout_year)2006                                                      0.207444
## as.factor(callout_year)2007                                                      0.205283
## as.factor(callout_year)2008                                                      0.206146
## as.factor(callout_year)2009                                                      0.207046
## as.factor(callout_year)2010                                                      0.204035
## as.factor(callout_year)2011                                                      0.207898
## as.factor(callout_dayofweek)monday                                               0.141457
## as.factor(callout_dayofweek)saturday                                             0.159221
## as.factor(callout_dayofweek)sunday                                               0.155919
## as.factor(callout_dayofweek)thursday                                             0.143115
## as.factor(callout_dayofweek)tuesday                                              0.140669
## as.factor(callout_dayofweek)wednesday                                            0.145760
## as.factor(callout_wardid == 1)TRUE                                               0.153086
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                      0.188279
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                      0.254355
## MED_SERVICETRUE                                                                  0.156027
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)    0.377964
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)    0.079690
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]    0.243742
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)   0.200020
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]   0.270608
##                                                                                z value
## (Intercept)                                                                     -8.176
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                  0.176
## cut2(oasis, g = 3)[27,34)                                                        3.100
## cut2(oasis, g = 3)[34,64]                                                        8.414
## cut2(age, g = 3)[56.1,73.8)                                                     -0.771
## cut2(age, g = 3)[73.8,91.4]                                                     -2.184
## female                                                                          -1.451
## request_tele                                                                    -2.719
## request_resp                                                                    -0.892
## request_mrsa                                                                     0.249
## request_vre                                                                      4.031
## request_cdiff                                                                    3.062
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                         4.647
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        10.086
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                      0.020
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                      5.077
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                      8.862
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                      7.816
## as.factor(callout_month)2                                                       -0.794
## as.factor(callout_month)3                                                        0.199
## as.factor(callout_month)4                                                       -0.376
## as.factor(callout_month)5                                                       -0.883
## as.factor(callout_month)6                                                       -1.216
## as.factor(callout_month)7                                                       -0.957
## as.factor(callout_month)8                                                       -0.389
## as.factor(callout_month)9                                                       -1.010
## as.factor(callout_month)10                                                      -1.693
## as.factor(callout_month)11                                                      -2.424
## as.factor(callout_month)12                                                      -0.901
## as.factor(callout_year)2006                                                     -0.560
## as.factor(callout_year)2007                                                     -1.429
## as.factor(callout_year)2008                                                     -2.171
## as.factor(callout_year)2009                                                     -2.535
## as.factor(callout_year)2010                                                     -1.857
## as.factor(callout_year)2011                                                     -3.353
## as.factor(callout_dayofweek)monday                                               0.906
## as.factor(callout_dayofweek)saturday                                            -0.122
## as.factor(callout_dayofweek)sunday                                               0.186
## as.factor(callout_dayofweek)thursday                                             0.390
## as.factor(callout_dayofweek)tuesday                                              1.347
## as.factor(callout_dayofweek)wednesday                                           -0.478
## as.factor(callout_wardid == 1)TRUE                                              -3.746
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                      0.896
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                      0.206
## MED_SERVICETRUE                                                                  1.190
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)    2.247
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)    1.209
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]    1.468
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)  -1.049
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]  -0.811
##                                                                                Pr(>|z|)
## (Intercept)                                                                    2.93e-16
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                0.860498
## cut2(oasis, g = 3)[27,34)                                                      0.001935
## cut2(oasis, g = 3)[34,64]                                                       < 2e-16
## cut2(age, g = 3)[56.1,73.8)                                                    0.440939
## cut2(age, g = 3)[73.8,91.4]                                                    0.028984
## female                                                                         0.146843
## request_tele                                                                   0.006544
## request_resp                                                                   0.372495
## request_mrsa                                                                   0.803695
## request_vre                                                                    5.55e-05
## request_cdiff                                                                  0.002198
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                       3.37e-06
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                    0.984050
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                    3.84e-07
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                     < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                    5.45e-15
## as.factor(callout_month)2                                                      0.427407
## as.factor(callout_month)3                                                      0.842127
## as.factor(callout_month)4                                                      0.706908
## as.factor(callout_month)5                                                      0.376968
## as.factor(callout_month)6                                                      0.224168
## as.factor(callout_month)7                                                      0.338756
## as.factor(callout_month)8                                                      0.697260
## as.factor(callout_month)9                                                      0.312649
## as.factor(callout_month)10                                                     0.090463
## as.factor(callout_month)11                                                     0.015341
## as.factor(callout_month)12                                                     0.367588
## as.factor(callout_year)2006                                                    0.575776
## as.factor(callout_year)2007                                                    0.152988
## as.factor(callout_year)2008                                                    0.029947
## as.factor(callout_year)2009                                                    0.011240
## as.factor(callout_year)2010                                                    0.063366
## as.factor(callout_year)2011                                                    0.000799
## as.factor(callout_dayofweek)monday                                             0.364750
## as.factor(callout_dayofweek)saturday                                           0.902717
## as.factor(callout_dayofweek)sunday                                             0.852242
## as.factor(callout_dayofweek)thursday                                           0.696321
## as.factor(callout_dayofweek)tuesday                                            0.178120
## as.factor(callout_dayofweek)wednesday                                          0.632620
## as.factor(callout_wardid == 1)TRUE                                             0.000179
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                    0.370278
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                    0.836565
## MED_SERVICETRUE                                                                0.234126
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)  0.024652
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)  0.226774
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]  0.142051
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.294094
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.417471
##                                                                                   
## (Intercept)                                                                    ***
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                   
## cut2(oasis, g = 3)[27,34)                                                      ** 
## cut2(oasis, g = 3)[34,64]                                                      ***
## cut2(age, g = 3)[56.1,73.8)                                                       
## cut2(age, g = 3)[73.8,91.4]                                                    *  
## female                                                                            
## request_tele                                                                   ** 
## request_resp                                                                      
## request_mrsa                                                                      
## request_vre                                                                    ***
## request_cdiff                                                                  ** 
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                       ***
## cut2(elixhauser_hospital, g = 3)[  7,31]                                       ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                       
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                    ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                    ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]                    ***
## as.factor(callout_month)2                                                         
## as.factor(callout_month)3                                                         
## as.factor(callout_month)4                                                         
## as.factor(callout_month)5                                                         
## as.factor(callout_month)6                                                         
## as.factor(callout_month)7                                                         
## as.factor(callout_month)8                                                         
## as.factor(callout_month)9                                                         
## as.factor(callout_month)10                                                     .  
## as.factor(callout_month)11                                                     *  
## as.factor(callout_month)12                                                        
## as.factor(callout_year)2006                                                       
## as.factor(callout_year)2007                                                       
## as.factor(callout_year)2008                                                    *  
## as.factor(callout_year)2009                                                    *  
## as.factor(callout_year)2010                                                    .  
## as.factor(callout_year)2011                                                    ***
## as.factor(callout_dayofweek)monday                                                
## as.factor(callout_dayofweek)saturday                                              
## as.factor(callout_dayofweek)sunday                                                
## as.factor(callout_dayofweek)thursday                                              
## as.factor(callout_dayofweek)tuesday                                               
## as.factor(callout_dayofweek)wednesday                                             
## as.factor(callout_wardid == 1)TRUE                                             ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                       
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                       
## MED_SERVICETRUE                                                                   
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)  *  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)     
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]     
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)    
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5682.0  on 9672  degrees of freedom
## Residual deviance: 5035.6  on 9623  degrees of freedom
## AIC: 5135.6
## 
## Number of Fisher Scoring iterations: 6
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5035.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5035.7
## cut2(oasis, g = 3)                                              2   5114.3
## cut2(age, g = 3)                                                2   5040.7
## female                                                          1   5037.7
## request_tele                                                    1   5043.2
## request_resp                                                    1   5036.5
## request_mrsa                                                    1   5035.7
## request_vre                                                     1   5050.6
## request_cdiff                                                   1   5044.4
## cut2(elixhauser_hospital, g = 3)                                2   5150.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5198.9
## as.factor(callout_month)                                       11   5046.9
## as.factor(callout_year)                                         6   5058.4
## as.factor(callout_dayofweek)                                    6   5040.4
## MED_SERVICE                                                     1   5037.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5042.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5036.9
##                                                                   AIC
## <none>                                                         5135.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5133.7
## cut2(oasis, g = 3)                                             5210.3
## cut2(age, g = 3)                                               5136.7
## female                                                         5135.7
## request_tele                                                   5141.2
## request_resp                                                   5134.5
## request_mrsa                                                   5133.7
## request_vre                                                    5148.6
## request_cdiff                                                  5142.4
## cut2(elixhauser_hospital, g = 3)                               5246.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5290.9
## as.factor(callout_month)                                       5124.9
## as.factor(callout_year)                                        5146.4
## as.factor(callout_dayofweek)                                   5128.4
## MED_SERVICE                                                    5135.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5136.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5132.9
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5035.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5035.7
## cut2(oasis, g = 3)                                              2   5114.3
## cut2(age, g = 3)                                                2   5040.7
## female                                                          1   5037.7
## request_tele                                                    1   5043.2
## request_resp                                                    1   5036.5
## request_mrsa                                                    1   5035.7
## request_vre                                                     1   5050.6
## request_cdiff                                                   1   5044.4
## cut2(elixhauser_hospital, g = 3)                                2   5150.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5198.9
## as.factor(callout_month)                                       11   5046.9
## as.factor(callout_year)                                         6   5058.4
## as.factor(callout_dayofweek)                                    6   5040.4
## MED_SERVICE                                                     1   5037.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5042.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5036.9
##                                                                   AIC
## <none>                                                         5135.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5133.7
## cut2(oasis, g = 3)                                             5210.3
## cut2(age, g = 3)                                               5136.7
## female                                                         5135.7
## request_tele                                                   5141.2
## request_resp                                                   5134.5
## request_mrsa                                                   5133.7
## request_vre                                                    5148.6
## request_cdiff                                                  5142.4
## cut2(elixhauser_hospital, g = 3)                               5246.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5290.9
## as.factor(callout_month)                                       5124.9
## as.factor(callout_year)                                        5146.4
## as.factor(callout_dayofweek)                                   5128.4
## MED_SERVICE                                                    5135.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5136.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5132.9
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.031
## cut2(oasis, g = 3)                                              78.704
## cut2(age, g = 3)                                                 5.083
## female                                                           2.109
## request_tele                                                     7.538
## request_resp                                                     0.869
## request_mrsa                                                     0.062
## request_vre                                                     14.966
## request_cdiff                                                    8.748
## cut2(elixhauser_hospital, g = 3)                               114.761
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     163.314
## as.factor(callout_month)                                        11.314
## as.factor(callout_year)                                         22.785
## as.factor(callout_dayofweek)                                     4.771
## MED_SERVICE                                                      1.462
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.021
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    1.277
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.8607004
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.0787439
## female                                                         0.1464668
## request_tele                                                   0.0060405
## request_resp                                                   0.3512004
## request_mrsa                                                   0.8040967
## request_vre                                                    0.0001094
## request_cdiff                                                  0.0030987
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.4173211
## as.factor(callout_year)                                        0.0008717
## as.factor(callout_dayofweek)                                   0.5734966
## MED_SERVICE                                                    0.2266808
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0712288
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.5280732
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_resp                                                      
## request_mrsa                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                      
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_mrsa"
## [1] 0.8040967
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 
##     1):cut2(PROPFULL_BEDS, c(0.9, 1))
##                                                                Df Deviance
## <none>                                                              5035.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5035.7
## cut2(oasis, g = 3)                                              2   5114.5
## cut2(age, g = 3)                                                2   5040.7
## female                                                          1   5037.8
## request_tele                                                    1   5043.2
## request_resp                                                    1   5036.6
## request_vre                                                     1   5051.4
## request_cdiff                                                   1   5044.4
## cut2(elixhauser_hospital, g = 3)                                2   5150.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5199.5
## as.factor(callout_month)                                       11   5047.0
## as.factor(callout_year)                                         6   5058.4
## as.factor(callout_dayofweek)                                    6   5040.5
## MED_SERVICE                                                     1   5037.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5042.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5037.0
##                                                                   AIC
## <none>                                                         5133.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5131.7
## cut2(oasis, g = 3)                                             5208.5
## cut2(age, g = 3)                                               5134.7
## female                                                         5133.8
## request_tele                                                   5139.2
## request_resp                                                   5132.6
## request_vre                                                    5147.4
## request_cdiff                                                  5140.4
## cut2(elixhauser_hospital, g = 3)                               5244.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5289.5
## as.factor(callout_month)                                       5123.0
## as.factor(callout_year)                                        5144.4
## as.factor(callout_dayofweek)                                   5126.5
## MED_SERVICE                                                    5133.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5134.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5131.0
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.035
## cut2(oasis, g = 3)                                              78.759
## cut2(age, g = 3)                                                 5.053
## female                                                           2.114
## request_tele                                                     7.537
## request_resp                                                     0.860
## request_vre                                                     15.690
## request_cdiff                                                    8.730
## cut2(elixhauser_hospital, g = 3)                               114.712
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     163.808
## as.factor(callout_month)                                        11.296
## as.factor(callout_year)                                         22.739
## as.factor(callout_dayofweek)                                     4.778
## MED_SERVICE                                                      1.487
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.009
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    1.280
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.8507003
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.0799442
## female                                                         0.1459262
## request_tele                                                   0.0060441
## request_resp                                                   0.3537129
## request_vre                                                    7.462e-05
## request_cdiff                                                  0.0031299
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.4188126
## as.factor(callout_year)                                        0.0008889
## as.factor(callout_dayofweek)                                   0.5725854
## MED_SERVICE                                                    0.2226188
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0716202
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.5273840
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_resp                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_dayofweek)                                      
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_dayofweek)"
## [1] 0.5725854
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, 
##     c(0.9, 1))
##                                                                Df Deviance
## <none>                                                              5040.5
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5040.5
## cut2(oasis, g = 3)                                              2   5119.8
## cut2(age, g = 3)                                                2   5045.6
## female                                                          1   5042.6
## request_tele                                                    1   5048.0
## request_resp                                                    1   5041.3
## request_vre                                                     1   5056.4
## request_cdiff                                                   1   5049.2
## cut2(elixhauser_hospital, g = 3)                                2   5155.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5205.3
## as.factor(callout_month)                                       11   5051.5
## as.factor(callout_year)                                         6   5063.4
## MED_SERVICE                                                     1   5041.9
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5047.9
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   2   5041.7
##                                                                   AIC
## <none>                                                         5126.5
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5124.5
## cut2(oasis, g = 3)                                             5201.8
## cut2(age, g = 3)                                               5127.6
## female                                                         5126.6
## request_tele                                                   5132.0
## request_resp                                                   5125.3
## request_vre                                                    5140.4
## request_cdiff                                                  5133.2
## cut2(elixhauser_hospital, g = 3)                               5237.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5283.3
## as.factor(callout_month)                                       5115.5
## as.factor(callout_year)                                        5137.4
## MED_SERVICE                                                    5125.9
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5127.9
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  5123.7
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.019
## cut2(oasis, g = 3)                                              79.284
## cut2(age, g = 3)                                                 5.082
## female                                                           2.137
## request_tele                                                     7.541
## request_resp                                                     0.867
## request_vre                                                     15.886
## request_cdiff                                                    8.726
## cut2(elixhauser_hospital, g = 3)                               115.133
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     164.822
## as.factor(callout_month)                                        11.005
## as.factor(callout_year)                                         22.888
## MED_SERVICE                                                      1.438
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.421
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    1.207
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.8903575
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.0787931
## female                                                         0.1437780
## request_tele                                                   0.0060302
## request_resp                                                   0.3517111
## request_vre                                                    6.728e-05
## request_cdiff                                                  0.0031375
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.4428466
## as.factor(callout_year)                                        0.0008348
## MED_SERVICE                                                    0.2304560
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0596222
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  0.5467630
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_resp                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))"
## [1] 0.546763
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5041.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5041.7
## cut2(oasis, g = 3)                                              2   5120.7
## cut2(age, g = 3)                                                2   5046.8
## female                                                          1   5043.7
## request_tele                                                    1   5049.2
## request_resp                                                    1   5042.6
## request_vre                                                     1   5057.6
## request_cdiff                                                   1   5050.7
## cut2(elixhauser_hospital, g = 3)                                2   5157.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5205.9
## as.factor(callout_month)                                       11   5052.7
## as.factor(callout_year)                                         6   5064.7
## as.factor(callout_wardid == 1)                                  1   5091.6
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  2   5042.8
## MED_SERVICE                                                     1   5043.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5048.9
##                                                                   AIC
## <none>                                                         5123.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5121.7
## cut2(oasis, g = 3)                                             5198.7
## cut2(age, g = 3)                                               5124.8
## female                                                         5123.7
## request_tele                                                   5129.2
## request_resp                                                   5122.6
## request_vre                                                    5137.6
## request_cdiff                                                  5130.7
## cut2(elixhauser_hospital, g = 3)                               5235.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5279.9
## as.factor(callout_month)                                       5112.7
## as.factor(callout_year)                                        5134.7
## as.factor(callout_wardid == 1)                                 5171.6
## cut2(PROPFULL_BEDS, c(0.9, 1))                                 5120.8
## MED_SERVICE                                                    5123.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5124.9
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.012
## cut2(oasis, g = 3)                                              79.039
## cut2(age, g = 3)                                                 5.146
## female                                                           2.063
## request_tele                                                     7.558
## request_resp                                                     0.914
## request_vre                                                     15.955
## request_cdiff                                                    8.977
## cut2(elixhauser_hospital, g = 3)                               116.095
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     164.171
## as.factor(callout_month)                                        11.011
## as.factor(callout_year)                                         22.987
## as.factor(callout_wardid == 1)                                  49.925
## cut2(PROPFULL_BEDS, c(0.9, 1))                                   1.153
## MED_SERVICE                                                      1.378
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.197
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.913085
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                                0.076309
## female                                                          0.150954
## request_tele                                                    0.005973
## request_resp                                                    0.339069
## request_vre                                                    6.486e-05
## request_cdiff                                                   0.002735
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                        0.442316
## as.factor(callout_year)                                         0.000801
## as.factor(callout_wardid == 1)                                 1.598e-12
## cut2(PROPFULL_BEDS, c(0.9, 1))                                  0.561851
## MED_SERVICE                                                     0.240476
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.065883
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_resp                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_wardid == 1)                                 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                                    
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(PROPFULL_BEDS, c(0.9, 1))"
## [1] 0.5618508
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5042.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5042.8
## cut2(oasis, g = 3)                                              2   5122.2
## cut2(age, g = 3)                                                2   5048.1
## female                                                          1   5044.9
## request_tele                                                    1   5050.2
## request_resp                                                    1   5043.8
## request_vre                                                     1   5059.1
## request_cdiff                                                   1   5051.6
## cut2(elixhauser_hospital, g = 3)                                2   5158.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5206.9
## as.factor(callout_month)                                       11   5053.9
## as.factor(callout_year)                                         6   5066.1
## as.factor(callout_wardid == 1)                                  1   5093.0
## MED_SERVICE                                                     1   5044.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5050.1
##                                                                   AIC
## <none>                                                         5120.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5118.8
## cut2(oasis, g = 3)                                             5196.2
## cut2(age, g = 3)                                               5122.1
## female                                                         5120.9
## request_tele                                                   5126.2
## request_resp                                                   5119.8
## request_vre                                                    5135.1
## request_cdiff                                                  5127.6
## cut2(elixhauser_hospital, g = 3)                               5232.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5276.9
## as.factor(callout_month)                                       5109.9
## as.factor(callout_year)                                        5132.1
## as.factor(callout_wardid == 1)                                 5169.0
## MED_SERVICE                                                    5120.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5122.1
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.000
## cut2(oasis, g = 3)                                              79.380
## cut2(age, g = 3)                                                 5.243
## female                                                           2.063
## request_tele                                                     7.399
## request_resp                                                     0.921
## request_vre                                                     16.223
## request_cdiff                                                    8.808
## cut2(elixhauser_hospital, g = 3)                               115.796
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     164.025
## as.factor(callout_month)                                        11.077
## as.factor(callout_year)                                         23.306
## as.factor(callout_wardid == 1)                                  50.177
## MED_SERVICE                                                      1.333
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.231
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    0.9884273
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                               0.0726970
## female                                                         0.1509506
## request_tele                                                   0.0065267
## request_resp                                                   0.3371746
## request_vre                                                    5.629e-05
## request_cdiff                                                  0.0029985
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_month)                                       0.4368298
## as.factor(callout_year)                                        0.0007003
## as.factor(callout_wardid == 1)                                 1.405e-12
## MED_SERVICE                                                    0.2483479
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0648942
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_resp                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_month)                                          
## as.factor(callout_year)                                        ***
## as.factor(callout_wardid == 1)                                 ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_month)"
## [1] 0.4368298
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_vre + request_cdiff + 
##     cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid == 
##     1) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5053.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5053.9
## cut2(oasis, g = 3)                                              2   5132.9
## cut2(age, g = 3)                                                2   5059.0
## female                                                          1   5056.1
## request_tele                                                    1   5061.3
## request_resp                                                    1   5054.9
## request_vre                                                     1   5069.8
## request_cdiff                                                   1   5062.9
## cut2(elixhauser_hospital, g = 3)                                2   5169.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5217.4
## as.factor(callout_year)                                         6   5075.2
## as.factor(callout_wardid == 1)                                  1   5104.3
## MED_SERVICE                                                     1   5055.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5061.0
##                                                                   AIC
## <none>                                                         5109.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5107.9
## cut2(oasis, g = 3)                                             5184.9
## cut2(age, g = 3)                                               5111.0
## female                                                         5110.1
## request_tele                                                   5115.3
## request_resp                                                   5108.9
## request_vre                                                    5123.8
## request_cdiff                                                  5116.9
## cut2(elixhauser_hospital, g = 3)                               5221.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5265.4
## as.factor(callout_year)                                        5119.2
## as.factor(callout_wardid == 1)                                 5158.3
## MED_SERVICE                                                    5109.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5111.0
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.005
## cut2(oasis, g = 3)                                              79.031
## cut2(age, g = 3)                                                 5.087
## female                                                           2.193
## request_tele                                                     7.355
## request_resp                                                     0.967
## request_vre                                                     15.860
## request_cdiff                                                    8.990
## cut2(elixhauser_hospital, g = 3)                               115.459
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     163.536
## as.factor(callout_year)                                         21.262
## as.factor(callout_wardid == 1)                                  50.437
## MED_SERVICE                                                      1.557
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.066
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.942685
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                                0.078597
## female                                                          0.138635
## request_tele                                                    0.006689
## request_resp                                                    0.325353
## request_vre                                                    6.821e-05
## request_cdiff                                                   0.002715
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_year)                                         0.001646
## as.factor(callout_wardid == 1)                                 1.230e-12
## MED_SERVICE                                                     0.212061
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.069828
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_resp                                                      
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_year)                                        ** 
## as.factor(callout_wardid == 1)                                 ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_resp"
## [1] 0.3253533
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5054.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5054.9
## cut2(oasis, g = 3)                                              2   5134.0
## cut2(age, g = 3)                                                2   5059.9
## female                                                          1   5057.0
## request_tele                                                    1   5062.2
## request_vre                                                     1   5070.8
## request_cdiff                                                   1   5063.8
## cut2(elixhauser_hospital, g = 3)                                2   5170.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5219.3
## as.factor(callout_year)                                         6   5076.5
## as.factor(callout_wardid == 1)                                  1   5105.3
## MED_SERVICE                                                     1   5056.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5062.1
##                                                                   AIC
## <none>                                                         5108.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5106.9
## cut2(oasis, g = 3)                                             5184.0
## cut2(age, g = 3)                                               5109.9
## female                                                         5109.0
## request_tele                                                   5114.2
## request_vre                                                    5122.8
## request_cdiff                                                  5115.8
## cut2(elixhauser_hospital, g = 3)                               5220.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5265.3
## as.factor(callout_year)                                        5118.5
## as.factor(callout_wardid == 1)                                 5157.3
## MED_SERVICE                                                    5108.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5110.1
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.004
## cut2(oasis, g = 3)                                              79.078
## cut2(age, g = 3)                                                 4.997
## female                                                           2.146
## request_tele                                                     7.288
## request_vre                                                     15.914
## request_cdiff                                                    8.961
## cut2(elixhauser_hospital, g = 3)                               115.392
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     164.461
## as.factor(callout_year)                                         21.600
## as.factor(callout_wardid == 1)                                  50.418
## MED_SERVICE                                                      1.526
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.185
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.949394
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                                0.082197
## female                                                          0.142917
## request_tele                                                    0.006943
## request_vre                                                    6.629e-05
## request_cdiff                                                   0.002758
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_year)                                         0.001430
## as.factor(callout_wardid == 1)                                 1.242e-12
## MED_SERVICE                                                     0.216783
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.066235
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_year)                                        ** 
## as.factor(callout_wardid == 1)                                 ***
## MED_SERVICE                                                       
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "MED_SERVICE"
## [1] 0.2167831
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5056.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5056.4
## cut2(oasis, g = 3)                                              2   5137.9
## cut2(age, g = 3)                                                2   5061.7
## female                                                          1   5058.6
## request_tele                                                    1   5063.8
## request_vre                                                     1   5072.5
## request_cdiff                                                   1   5065.4
## cut2(elixhauser_hospital, g = 3)                                2   5173.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5219.4
## as.factor(callout_year)                                         6   5078.2
## as.factor(callout_wardid == 1)                                  1   5105.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5063.6
##                                                                   AIC
## <none>                                                         5108.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5106.4
## cut2(oasis, g = 3)                                             5185.9
## cut2(age, g = 3)                                               5109.7
## female                                                         5108.6
## request_tele                                                   5113.8
## request_vre                                                    5122.5
## request_cdiff                                                  5115.4
## cut2(elixhauser_hospital, g = 3)                               5221.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5263.4
## as.factor(callout_year)                                        5118.2
## as.factor(callout_wardid == 1)                                 5155.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5109.6
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.002
## cut2(oasis, g = 3)                                              81.467
## cut2(age, g = 3)                                                 5.315
## female                                                           2.190
## request_tele                                                     7.363
## request_vre                                                     16.138
## request_cdiff                                                    8.986
## cut2(elixhauser_hospital, g = 3)                               117.126
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     162.980
## as.factor(callout_year)                                         21.842
## as.factor(callout_wardid == 1)                                  48.977
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.227
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.960419
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                                0.070133
## female                                                          0.138896
## request_tele                                                    0.006656
## request_vre                                                     5.89e-05
## request_cdiff                                                   0.002721
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_year)                                         0.001293
## as.factor(callout_wardid == 1)                                  2.59e-12
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.065003
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## female                                                            
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_year)                                        ** 
## as.factor(callout_wardid == 1)                                 ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "female"
## [1] 0.1388955
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.000,12.000)")
##                                                                Df Deviance
## <none>                                                              5058.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     1   5058.6
## cut2(oasis, g = 3)                                              2   5139.4
## cut2(age, g = 3)                                                2   5064.3
## request_tele                                                    1   5066.0
## request_vre                                                     1   5074.4
## request_cdiff                                                   1   5067.5
## cut2(elixhauser_hospital, g = 3)                                2   5179.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                      4   5221.1
## as.factor(callout_year)                                         6   5080.1
## as.factor(callout_wardid == 1)                                  1   5107.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  3   5065.9
##                                                                   AIC
## <none>                                                         5108.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    5106.6
## cut2(oasis, g = 3)                                             5185.4
## cut2(age, g = 3)                                               5110.3
## request_tele                                                   5114.0
## request_vre                                                    5122.4
## request_cdiff                                                  5115.5
## cut2(elixhauser_hospital, g = 3)                               5225.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     5263.1
## as.factor(callout_year)                                        5118.1
## as.factor(callout_wardid == 1)                                 5155.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5109.9
##                                                                    LRT
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")      0.000
## cut2(oasis, g = 3)                                              80.830
## cut2(age, g = 3)                                                 5.657
## request_tele                                                     7.408
## request_vre                                                     15.772
## request_cdiff                                                    8.880
## cut2(elixhauser_hospital, g = 3)                               120.475
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     162.464
## as.factor(callout_year)                                         21.480
## as.factor(callout_wardid == 1)                                  48.770
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")   7.309
##                                                                 Pr(>Chi)
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")     0.997572
## cut2(oasis, g = 3)                                             < 2.2e-16
## cut2(age, g = 3)                                                0.059102
## request_tele                                                    0.006495
## request_vre                                                    7.145e-05
## request_cdiff                                                   0.002883
## cut2(elixhauser_hospital, g = 3)                               < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     < 2.2e-16
## as.factor(callout_year)                                         0.001504
## as.factor(callout_wardid == 1)                                 2.878e-12
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")  0.062683
##                                                                   
## <none>                                                            
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")       
## cut2(oasis, g = 3)                                             ***
## cut2(age, g = 3)                                               .  
## request_tele                                                   ** 
## request_vre                                                    ***
## request_cdiff                                                  ** 
## cut2(elixhauser_hospital, g = 3)                               ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     ***
## as.factor(callout_year)                                        ** 
## as.factor(callout_wardid == 1)                                 ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "relevel(cut2(hourofcallout2, c(7, 12, 19)), \"[ 7.000,12.000)\")"
## [1] 0.06268348
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital, 
##     g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1)
##                                                             Df Deviance
## <none>                                                           5065.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1   5065.9
## cut2(oasis, g = 3)                                           2   5147.7
## cut2(age, g = 3)                                             2   5071.8
## request_tele                                                 1   5073.1
## request_vre                                                  1   5082.0
## request_cdiff                                                1   5074.6
## cut2(elixhauser_hospital, g = 3)                             2   5186.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4   5229.0
## as.factor(callout_year)                                      6   5086.2
## as.factor(callout_wardid == 1)                               1   5114.3
##                                                                AIC     LRT
## <none>                                                      5109.9        
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5107.9   0.005
## cut2(oasis, g = 3)                                          5187.7  81.785
## cut2(age, g = 3)                                            5111.8   5.870
## request_tele                                                5115.1   7.174
## request_vre                                                 5124.0  16.053
## request_cdiff                                               5116.6   8.662
## cut2(elixhauser_hospital, g = 3)                            5226.4 120.525
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  5265.0 163.079
## as.factor(callout_year)                                     5118.2  20.332
## as.factor(callout_wardid == 1)                              5156.3  48.382
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  0.944042    
## cut2(oasis, g = 3)                                          < 2.2e-16 ***
## cut2(age, g = 3)                                             0.053129 .  
## request_tele                                                 0.007397 ** 
## request_vre                                                 6.161e-05 ***
## request_cdiff                                                0.003250 ** 
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_year)                                      0.002416 ** 
## as.factor(callout_wardid == 1)                              3.508e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "cut2(age, g = 3)"
## [1] 0.05312853
## Single term deletions
## 
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + request_tele + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) + 
##     as.factor(callout_wardid == 1)
##                                                             Df Deviance
## <none>                                                           5071.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1   5071.8
## cut2(oasis, g = 3)                                           2   5147.8
## request_tele                                                 1   5080.2
## request_vre                                                  1   5088.5
## request_cdiff                                                1   5079.8
## cut2(elixhauser_hospital, g = 3)                             2   5187.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4   5242.1
## as.factor(callout_year)                                      6   5092.3
## as.factor(callout_wardid == 1)                               1   5124.0
##                                                                AIC     LRT
## <none>                                                      5111.8        
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5109.8   0.015
## cut2(oasis, g = 3)                                          5183.8  76.061
## request_tele                                                5118.2   8.408
## request_vre                                                 5126.5  16.682
## request_cdiff                                               5117.8   8.014
## cut2(elixhauser_hospital, g = 3)                            5223.0 115.266
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  5274.1 170.337
## as.factor(callout_year)                                     5120.3  20.519
## as.factor(callout_wardid == 1)                              5162.0  52.263
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  0.903510    
## cut2(oasis, g = 3)                                          < 2.2e-16 ***
## request_tele                                                 0.003736 ** 
## request_vre                                                 4.419e-05 ***
## request_cdiff                                                0.004641 ** 
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_year)                                      0.002238 ** 
## as.factor(callout_wardid == 1)                              4.855e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_cdiff"
## [1] 0.004641178
    I(HOSP_FREE_DAYS <= 7)
    Odds Ratio CI p
(Intercept)   0.06 0.04 – 0.09 <.001
I(cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]”)   1.01 0.80 – 1.28 .903
cut2(oasis, g = 3)
[27,34)   1.35 1.10 – 1.65 .003
[34,64]   2.26 1.87 – 2.75 <.001
request_tele   0.79 0.67 – 0.93 .004
request_vre   1.80 1.37 – 2.34 <.001
request_cdiff   1.51 1.14 – 1.97 .003
cut2(elixhauser_hospital, g = 3)
[ 1, 7)   1.67 1.35 – 2.08 <.001
[ 7,31]   2.80 2.30 – 3.42 <.001
cut2(los_pre_callout_days, c(1, 3, 7, 28))
[ 1.000, 3.000)   1.01 0.82 – 1.26 .928
[ 3.000, 7.000)   1.81 1.45 – 2.28 <.001
[ 7.000, 28.000)   2.96 2.35 – 3.75 <.001
[ 28.000,130.762]   11.26 6.23 – 20.37 <.001
as.factor(callout_year)
2006   0.95 0.65 – 1.43 .813
2007   0.80 0.55 – 1.20 .270
2008   0.68 0.46 – 1.01 .049
2009   0.65 0.45 – 0.97 .031
2010   0.73 0.50 – 1.09 .112
2011   0.54 0.37 – 0.81 .003
as.factor(callout_wardid == 1) (TRUE)   0.50 0.42 – 0.60 <.001
Observations   9673
##                                                                       
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE  1.015
## cut2(oasis, g = 3)[27,34)                                        1.347
## cut2(oasis, g = 3)[34,64]                                        2.264
## request_tele                                                     0.790
## request_vre                                                      1.798
## request_cdiff                                                    1.507
## cut2(elixhauser_hospital, g = 3)[  1, 7)                         1.674
## cut2(elixhauser_hospital, g = 3)[  7,31]                         2.796
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)      1.010
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)      1.814
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)      2.965
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     11.258
## as.factor(callout_year)2006                                      0.954
## as.factor(callout_year)2007                                      0.805
## as.factor(callout_year)2008                                      0.676
## as.factor(callout_year)2009                                      0.653
## as.factor(callout_year)2010                                      0.732
## as.factor(callout_year)2011                                      0.544
## as.factor(callout_wardid == 1)TRUE                               0.501
##                                                                 2.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.799
## cut2(oasis, g = 3)[27,34)                                       1.104
## cut2(oasis, g = 3)[34,64]                                       1.867
## request_tele                                                    0.672
## request_vre                                                     1.367
## request_cdiff                                                   1.138
## cut2(elixhauser_hospital, g = 3)[  1, 7)                        1.348
## cut2(elixhauser_hospital, g = 3)[  7,31]                        2.296
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)     0.815
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)     1.448
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)     2.347
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     6.231
## as.factor(callout_year)2006                                     0.647
## as.factor(callout_year)2007                                     0.551
## as.factor(callout_year)2008                                     0.462
## as.factor(callout_year)2009                                     0.446
## as.factor(callout_year)2010                                     0.503
## as.factor(callout_year)2011                                     0.369
## as.factor(callout_wardid == 1)TRUE                              0.419
##                                                                 97.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE  1.278
## cut2(oasis, g = 3)[27,34)                                        1.646
## cut2(oasis, g = 3)[34,64]                                        2.752
## request_tele                                                     0.927
## request_vre                                                      2.340
## request_cdiff                                                    1.972
## cut2(elixhauser_hospital, g = 3)[  1, 7)                         2.083
## cut2(elixhauser_hospital, g = 3)[  7,31]                         3.423
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)      1.256
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)      2.278
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)      3.754
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     20.367
## as.factor(callout_year)2006                                      1.429
## as.factor(callout_year)2007                                      1.196
## as.factor(callout_year)2008                                      1.007
## as.factor(callout_year)2009                                      0.972
## as.factor(callout_year)2010                                      1.086
## as.factor(callout_year)2011                                      0.815
## as.factor(callout_wardid == 1)TRUE                               0.601
##                                                                      
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.903
## cut2(oasis, g = 3)[27,34)                                       0.003
## cut2(oasis, g = 3)[34,64]                                       0.000
## request_tele                                                    0.004
## request_vre                                                     0.000
## request_cdiff                                                   0.003
## cut2(elixhauser_hospital, g = 3)[  1, 7)                        0.000
## cut2(elixhauser_hospital, g = 3)[  7,31]                        0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)     0.928
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)     0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)     0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762]     0.000
## as.factor(callout_year)2006                                     0.813
## as.factor(callout_year)2007                                     0.270
## as.factor(callout_year)2008                                     0.049
## as.factor(callout_year)2009                                     0.031
## as.factor(callout_year)2010                                     0.112
## as.factor(callout_year)2011                                     0.003
## as.factor(callout_wardid == 1)TRUE                              0.000

Answer 5: After adjusting for potential confounders, there is no statistically significant evidence that a long delay yield lower rates of poor HFD outcome (<7 days).

Question 6: Do survivors of the hospital stay who have long discharge delays have longer log(LOS_post_icu)?

Post ICU days were similar in the above table 5.95 vs 6.19 (p=0.35)

## Single term deletions
## 
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.00,12.00)")
##                                                               Df Sum of Sq
## <none>                                                                    
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    1    14.748
## cut2(oasis, g = 3)                                             2     8.292
## cut2(age, g = 3)                                               2     9.628
## female                                                         1     3.869
## request_tele                                                   1     4.450
## request_resp                                                   1     2.611
## request_mrsa                                                   1    14.776
## request_vre                                                    1     8.546
## request_cdiff                                                  1     6.112
## cut2(elixhauser_hospital, g = 3)                               2    71.619
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     4   245.095
## as.factor(callout_month)                                      11     5.600
## as.factor(callout_year)                                        6     4.835
## as.factor(callout_dayofweek)                                   6    14.780
## MED_SERVICE                                                    1     2.779
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")   3     3.920
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  2     0.126
##                                                                  RSS
## <none>                                                        4451.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   4466.3
## cut2(oasis, g = 3)                                            4459.9
## cut2(age, g = 3)                                              4461.2
## female                                                        4455.4
## request_tele                                                  4456.0
## request_resp                                                  4454.2
## request_mrsa                                                  4466.3
## request_vre                                                   4460.1
## request_cdiff                                                 4457.7
## cut2(elixhauser_hospital, g = 3)                              4523.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    4696.7
## as.factor(callout_month)                                      4457.2
## as.factor(callout_year)                                       4456.4
## as.factor(callout_dayofweek)                                  4466.3
## MED_SERVICE                                                   4454.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  4455.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 4451.7
##                                                                   AIC
## <none>                                                        -6475.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   -6447.1
## cut2(oasis, g = 3)                                            -6462.3
## cut2(age, g = 3)                                              -6459.6
## female                                                        -6469.4
## request_tele                                                  -6468.2
## request_resp                                                  -6472.0
## request_mrsa                                                  -6447.1
## request_vre                                                   -6459.8
## request_cdiff                                                 -6464.8
## cut2(elixhauser_hospital, g = 3)                              -6333.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    -5993.5
## as.factor(callout_month)                                      -6485.9
## as.factor(callout_year)                                       -6477.4
## as.factor(callout_dayofweek)                                  -6457.1
## MED_SERVICE                                                   -6471.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  -6473.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) -6479.1
##                                                                F value
## <none>                                                                
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    30.1152
## cut2(oasis, g = 3)                                              8.4661
## cut2(age, g = 3)                                                9.8302
## female                                                          7.9010
## request_tele                                                    9.0862
## request_resp                                                    5.3319
## request_mrsa                                                   30.1732
## request_vre                                                    17.4513
## request_cdiff                                                  12.4797
## cut2(elixhauser_hospital, g = 3)                               73.1227
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    125.1194
## as.factor(callout_month)                                        1.0396
## as.factor(callout_year)                                         1.6456
## as.factor(callout_dayofweek)                                    5.0301
## MED_SERVICE                                                     5.6742
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")    2.6685
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))   0.1289
##                                                                  Pr(>F)
## <none>                                                                 
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   4.181e-08
## cut2(oasis, g = 3)                                            0.0002121
## cut2(age, g = 3)                                              5.438e-05
## female                                                        0.0049513
## request_tele                                                  0.0025825
## request_resp                                                  0.0209614
## request_mrsa                                                  4.058e-08
## request_vre                                                   2.975e-05
## request_cdiff                                                 0.0004134
## cut2(elixhauser_hospital, g = 3)                              < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    < 2.2e-16
## as.factor(callout_month)                                      0.4076231
## as.factor(callout_year)                                       0.1302292
## as.factor(callout_dayofweek)                                  3.701e-05
## MED_SERVICE                                                   0.0172370
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  0.0459630
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.8790819
##                                                                  
## <none>                                                           
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   ***
## cut2(oasis, g = 3)                                            ***
## cut2(age, g = 3)                                              ***
## female                                                        ** 
## request_tele                                                  ** 
## request_resp                                                  *  
## request_mrsa                                                  ***
## request_vre                                                   ***
## request_cdiff                                                 ***
## cut2(elixhauser_hospital, g = 3)                              ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    ***
## as.factor(callout_month)                                         
## as.factor(callout_year)                                          
## as.factor(callout_dayofweek)                                  ***
## MED_SERVICE                                                   *  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  *  
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(age, g = 3) + request_mrsa + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_wardid == 
##     1)
##                                                             Df Sum of Sq
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1    16.573
## cut2(age, g = 3)                                             2    14.802
## request_mrsa                                                 1    13.941
## request_vre                                                  1     9.047
## request_cdiff                                                1     7.102
## cut2(elixhauser_hospital, g = 3)                             2    72.721
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4   258.909
## as.factor(callout_wardid == 1)                               1    41.927
##                                                                RSS     AIC
## <none>                                                      4510.1 -6428.0
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4526.7 -6396.5
## cut2(age, g = 3)                                            4524.9 -6402.0
## request_mrsa                                                4524.0 -6401.8
## request_vre                                                 4519.1 -6411.7
## request_cdiff                                               4517.2 -6415.6
## cut2(elixhauser_hospital, g = 3)                            4582.8 -6285.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  4769.0 -5925.8
## as.factor(callout_wardid == 1)                              4552.0 -6345.4
##                                                             F value
## <none>                                                             
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  33.534
## cut2(age, g = 3)                                             14.976
## request_mrsa                                                 28.208
## request_vre                                                  18.306
## request_cdiff                                                14.371
## cut2(elixhauser_hospital, g = 3)                             73.574
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  130.973
## as.factor(callout_wardid == 1)                               84.837
##                                                                Pr(>F)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 7.234e-09 ***
## cut2(age, g = 3)                                            3.212e-07 ***
## request_mrsa                                                1.115e-07 ***
## request_vre                                                 1.901e-05 ***
## request_cdiff                                               0.0001511 ***
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_wardid == 1)                              < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Single term deletions
## 
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.00,12.00)")
##                                                              Df Sum of Sq
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   1    14.596
## cut2(oasis, g = 3)                                            2     8.081
## cut2(age, g = 3)                                              2     9.483
## female                                                        1     4.071
## request_tele                                                  1     4.283
## request_resp                                                  1     2.489
## request_mrsa                                                  1    14.469
## request_vre                                                   1     8.732
## request_cdiff                                                 1     6.378
## cut2(elixhauser_hospital, g = 3)                              2    71.836
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    4   248.341
## as.factor(callout_dayofweek)                                  6    15.868
## as.factor(callout_wardid == 1)                                1    35.367
## cut2(PROPFULL_BEDS, c(0.9, 1))                                2     3.827
## MED_SERVICE                                                   1     2.477
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  3     3.551
##                                                                 RSS
## <none>                                                       4462.2
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  4476.8
## cut2(oasis, g = 3)                                           4470.3
## cut2(age, g = 3)                                             4471.7
## female                                                       4466.3
## request_tele                                                 4466.5
## request_resp                                                 4464.7
## request_mrsa                                                 4476.7
## request_vre                                                  4471.0
## request_cdiff                                                4468.6
## cut2(elixhauser_hospital, g = 3)                             4534.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4710.6
## as.factor(callout_dayofweek)                                 4478.1
## as.factor(callout_wardid == 1)                               4497.6
## cut2(PROPFULL_BEDS, c(0.9, 1))                               4466.0
## MED_SERVICE                                                  4464.7
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4465.8
##                                                                  AIC
## <none>                                                       -6491.5
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  -6463.7
## cut2(oasis, g = 3)                                           -6479.0
## cut2(age, g = 3)                                             -6476.1
## female                                                       -6485.2
## request_tele                                                 -6484.7
## request_resp                                                 -6488.4
## request_mrsa                                                 -6463.9
## request_vre                                                  -6475.6
## request_cdiff                                                -6480.5
## cut2(elixhauser_hospital, g = 3)                             -6349.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   -6004.5
## as.factor(callout_dayofweek)                                 -6471.1
## as.factor(callout_wardid == 1)                               -6421.4
## cut2(PROPFULL_BEDS, c(0.9, 1))                               -6487.7
## MED_SERVICE                                                  -6488.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6490.2
##                                                               F value
## <none>                                                               
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   29.7952
## cut2(oasis, g = 3)                                             8.2484
## cut2(age, g = 3)                                               9.6789
## female                                                         8.3101
## request_tele                                                   8.7426
## request_resp                                                   5.0808
## request_mrsa                                                  29.5358
## request_vre                                                   17.8246
## request_cdiff                                                 13.0199
## cut2(elixhauser_hospital, g = 3)                              73.3220
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   126.7384
## as.factor(callout_dayofweek)                                   5.3987
## as.factor(callout_wardid == 1)                                72.1964
## cut2(PROPFULL_BEDS, c(0.9, 1))                                 3.9061
## MED_SERVICE                                                    5.0557
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")   2.4160
##                                                                 Pr(>F)    
## <none>                                                                    
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  4.928e-08 ***
## cut2(oasis, g = 3)                                           0.0002636 ***
## cut2(age, g = 3)                                             6.324e-05 ***
## female                                                       0.0039517 ** 
## request_tele                                                 0.0031166 ** 
## request_resp                                                 0.0242159 *  
## request_mrsa                                                 5.631e-08 ***
## request_vre                                                  2.446e-05 ***
## request_cdiff                                                0.0003098 ***
## cut2(elixhauser_hospital, g = 3)                             < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   < 2.2e-16 ***
## as.factor(callout_dayofweek)                                 1.403e-05 ***
## as.factor(callout_wardid == 1)                               < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                               0.0201523 *  
## MED_SERVICE                                                  0.0245682 *  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0644688 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lm(formula = log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, 
##     c(24)) == "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, 
##     g = 3) + female + request_tele + request_resp + request_mrsa + 
##     request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) + 
##     cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) + 
##     as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9, 
##         1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 
##     12, 19)), "[ 7.00,12.00)"), data = (d %>% filter(hospitaldeath == 
##     0)))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6645 -0.4599 -0.0669  0.4172  3.1951 
## 
## Coefficients:
##                                                                                 Estimate
## (Intercept)                                                                     1.489586
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                -0.139340
## cut2(oasis, g = 3)[27,33)                                                       0.054026
## cut2(oasis, g = 3)[33,62]                                                       0.075975
## cut2(age, g = 3)[55.8,73.4)                                                     0.080937
## cut2(age, g = 3)[73.4,91.4]                                                     0.034401
## female                                                                          0.041595
## request_tele                                                                    0.046959
## request_resp                                                                    0.132692
## request_mrsa                                                                    0.122078
## request_vre                                                                     0.146393
## request_cdiff                                                                   0.123316
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                        0.109199
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        0.225272
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                     0.087810
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                     0.283111
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                     0.543639
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022]                     0.738618
## as.factor(callout_month)2                                                      -0.009112
## as.factor(callout_month)3                                                      -0.062380
## as.factor(callout_month)4                                                      -0.006644
## as.factor(callout_month)5                                                       0.009055
## as.factor(callout_month)6                                                      -0.038348
## as.factor(callout_month)7                                                      -0.034477
## as.factor(callout_month)8                                                      -0.056856
## as.factor(callout_month)9                                                      -0.025600
## as.factor(callout_month)10                                                     -0.056898
## as.factor(callout_month)11                                                     -0.065643
## as.factor(callout_month)12                                                     -0.053432
## as.factor(callout_year)2006                                                     0.067230
## as.factor(callout_year)2007                                                     0.057870
## as.factor(callout_year)2008                                                     0.056158
## as.factor(callout_year)2009                                                    -0.004633
## as.factor(callout_year)2010                                                     0.040462
## as.factor(callout_year)2011                                                     0.031787
## as.factor(callout_dayofweek)monday                                             -0.112845
## as.factor(callout_dayofweek)saturday                                           -0.037197
## as.factor(callout_dayofweek)sunday                                             -0.073449
## as.factor(callout_dayofweek)thursday                                           -0.031553
## as.factor(callout_dayofweek)tuesday                                            -0.120120
## as.factor(callout_dayofweek)wednesday                                          -0.055300
## as.factor(callout_wardid == 1)TRUE                                             -0.185955
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                    -0.021342
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                    -0.067086
## MED_SERVICETRUE                                                                -0.070920
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00)       0.084643
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00)       0.042077
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87]       0.032486
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)  0.003363
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.026629
##                                                                                Std. Error
## (Intercept)                                                                      0.068191
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                  0.025391
## cut2(oasis, g = 3)[27,33)                                                        0.018359
## cut2(oasis, g = 3)[33,62]                                                        0.019147
## cut2(age, g = 3)[55.8,73.4)                                                      0.018476
## cut2(age, g = 3)[73.4,91.4]                                                      0.019800
## female                                                                           0.014798
## request_tele                                                                     0.015579
## request_resp                                                                     0.057465
## request_mrsa                                                                     0.022224
## request_vre                                                                      0.035043
## request_cdiff                                                                    0.034907
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                         0.018088
## cut2(elixhauser_hospital, g = 3)[  7,31]                                         0.018637
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                      0.018030
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                      0.022451
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                      0.027327
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022]                      0.121824
## as.factor(callout_month)2                                                        0.037111
## as.factor(callout_month)3                                                        0.037385
## as.factor(callout_month)4                                                        0.037362
## as.factor(callout_month)5                                                        0.036480
## as.factor(callout_month)6                                                        0.037074
## as.factor(callout_month)7                                                        0.036642
## as.factor(callout_month)8                                                        0.035831
## as.factor(callout_month)9                                                        0.036022
## as.factor(callout_month)10                                                       0.035486
## as.factor(callout_month)11                                                       0.036287
## as.factor(callout_month)12                                                       0.035813
## as.factor(callout_year)2006                                                      0.044610
## as.factor(callout_year)2007                                                      0.043576
## as.factor(callout_year)2008                                                      0.042974
## as.factor(callout_year)2009                                                      0.043142
## as.factor(callout_year)2010                                                      0.042956
## as.factor(callout_year)2011                                                      0.042742
## as.factor(callout_dayofweek)monday                                               0.027696
## as.factor(callout_dayofweek)saturday                                             0.029790
## as.factor(callout_dayofweek)sunday                                               0.029940
## as.factor(callout_dayofweek)thursday                                             0.027665
## as.factor(callout_dayofweek)tuesday                                              0.027675
## as.factor(callout_dayofweek)wednesday                                            0.027301
## as.factor(callout_wardid == 1)TRUE                                               0.033790
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                      0.044248
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                      0.059726
## MED_SERVICETRUE                                                                  0.029773
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00)        0.094514
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00)        0.015504
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87]        0.050599
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)   0.045804
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]   0.061602
##                                                                                t value
## (Intercept)                                                                     21.844
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                 -5.488
## cut2(oasis, g = 3)[27,33)                                                        2.943
## cut2(oasis, g = 3)[33,62]                                                        3.968
## cut2(age, g = 3)[55.8,73.4)                                                      4.381
## cut2(age, g = 3)[73.4,91.4]                                                      1.737
## female                                                                           2.811
## request_tele                                                                     3.014
## request_resp                                                                     2.309
## request_mrsa                                                                     5.493
## request_vre                                                                      4.177
## request_cdiff                                                                    3.533
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                         6.037
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        12.087
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                      4.870
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                     12.610
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                     19.894
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022]                      6.063
## as.factor(callout_month)2                                                       -0.246
## as.factor(callout_month)3                                                       -1.669
## as.factor(callout_month)4                                                       -0.178
## as.factor(callout_month)5                                                        0.248
## as.factor(callout_month)6                                                       -1.034
## as.factor(callout_month)7                                                       -0.941
## as.factor(callout_month)8                                                       -1.587
## as.factor(callout_month)9                                                       -0.711
## as.factor(callout_month)10                                                      -1.603
## as.factor(callout_month)11                                                      -1.809
## as.factor(callout_month)12                                                      -1.492
## as.factor(callout_year)2006                                                      1.507
## as.factor(callout_year)2007                                                      1.328
## as.factor(callout_year)2008                                                      1.307
## as.factor(callout_year)2009                                                     -0.107
## as.factor(callout_year)2010                                                      0.942
## as.factor(callout_year)2011                                                      0.744
## as.factor(callout_dayofweek)monday                                              -4.074
## as.factor(callout_dayofweek)saturday                                            -1.249
## as.factor(callout_dayofweek)sunday                                              -2.453
## as.factor(callout_dayofweek)thursday                                            -1.141
## as.factor(callout_dayofweek)tuesday                                             -4.340
## as.factor(callout_dayofweek)wednesday                                           -2.026
## as.factor(callout_wardid == 1)TRUE                                              -5.503
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                     -0.482
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                     -1.123
## MED_SERVICETRUE                                                                 -2.382
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00)        0.896
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00)        2.714
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87]        0.642
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)   0.073
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]  -0.432
##                                                                                Pr(>|t|)
## (Intercept)                                                                     < 2e-16
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                4.18e-08
## cut2(oasis, g = 3)[27,33)                                                      0.003262
## cut2(oasis, g = 3)[33,62]                                                      7.31e-05
## cut2(age, g = 3)[55.8,73.4)                                                    1.20e-05
## cut2(age, g = 3)[73.4,91.4]                                                    0.082354
## female                                                                         0.004951
## request_tele                                                                   0.002583
## request_resp                                                                   0.020961
## request_mrsa                                                                   4.06e-08
## request_vre                                                                    2.98e-05
## request_cdiff                                                                  0.000413
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                       1.63e-09
## cut2(elixhauser_hospital, g = 3)[  7,31]                                        < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                    1.13e-06
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                     < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                     < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022]                    1.39e-09
## as.factor(callout_month)2                                                      0.806043
## as.factor(callout_month)3                                                      0.095231
## as.factor(callout_month)4                                                      0.858860
## as.factor(callout_month)5                                                      0.803979
## as.factor(callout_month)6                                                      0.300984
## as.factor(callout_month)7                                                      0.346773
## as.factor(callout_month)8                                                      0.112597
## as.factor(callout_month)9                                                      0.477303
## as.factor(callout_month)10                                                     0.108881
## as.factor(callout_month)11                                                     0.070488
## as.factor(callout_month)12                                                     0.135741
## as.factor(callout_year)2006                                                    0.131830
## as.factor(callout_year)2007                                                    0.184203
## as.factor(callout_year)2008                                                    0.191316
## as.factor(callout_year)2009                                                    0.914489
## as.factor(callout_year)2010                                                    0.346241
## as.factor(callout_year)2011                                                    0.457083
## as.factor(callout_dayofweek)monday                                             4.65e-05
## as.factor(callout_dayofweek)saturday                                           0.211824
## as.factor(callout_dayofweek)sunday                                             0.014177
## as.factor(callout_dayofweek)thursday                                           0.254088
## as.factor(callout_dayofweek)tuesday                                            1.44e-05
## as.factor(callout_dayofweek)wednesday                                          0.042835
## as.factor(callout_wardid == 1)TRUE                                             3.83e-08
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                    0.629580
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                    0.261371
## MED_SERVICETRUE                                                                0.017237
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00)      0.370510
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00)      0.006660
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87]      0.520873
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.941479
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.665558
##                                                                                   
## (Intercept)                                                                    ***
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE                ***
## cut2(oasis, g = 3)[27,33)                                                      ** 
## cut2(oasis, g = 3)[33,62]                                                      ***
## cut2(age, g = 3)[55.8,73.4)                                                    ***
## cut2(age, g = 3)[73.4,91.4]                                                    .  
## female                                                                         ** 
## request_tele                                                                   ** 
## request_resp                                                                   *  
## request_mrsa                                                                   ***
## request_vre                                                                    ***
## request_cdiff                                                                  ***
## cut2(elixhauser_hospital, g = 3)[  1, 7)                                       ***
## cut2(elixhauser_hospital, g = 3)[  7,31]                                       ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)                    ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)                    ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)                    ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022]                    ***
## as.factor(callout_month)2                                                         
## as.factor(callout_month)3                                                      .  
## as.factor(callout_month)4                                                         
## as.factor(callout_month)5                                                         
## as.factor(callout_month)6                                                         
## as.factor(callout_month)7                                                         
## as.factor(callout_month)8                                                         
## as.factor(callout_month)9                                                         
## as.factor(callout_month)10                                                        
## as.factor(callout_month)11                                                     .  
## as.factor(callout_month)12                                                        
## as.factor(callout_year)2006                                                       
## as.factor(callout_year)2007                                                       
## as.factor(callout_year)2008                                                       
## as.factor(callout_year)2009                                                       
## as.factor(callout_year)2010                                                       
## as.factor(callout_year)2011                                                       
## as.factor(callout_dayofweek)monday                                             ***
## as.factor(callout_dayofweek)saturday                                              
## as.factor(callout_dayofweek)sunday                                             *  
## as.factor(callout_dayofweek)thursday                                              
## as.factor(callout_dayofweek)tuesday                                            ***
## as.factor(callout_dayofweek)wednesday                                          *  
## as.factor(callout_wardid == 1)TRUE                                             ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                                       
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                                       
## MED_SERVICETRUE                                                                *  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00)         
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00)      ** 
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87]         
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)    
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6998 on 9090 degrees of freedom
## Multiple R-squared:  0.1225, Adjusted R-squared:  0.1178 
## F-statistic:  25.9 on 49 and 9090 DF,  p-value: < 2.2e-16
## Single term deletions
## 
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.00,12.00)")
##                                                               Df Sum of Sq
## <none>                                                                    
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    1    14.748
## cut2(oasis, g = 3)                                             2     8.292
## cut2(age, g = 3)                                               2     9.628
## female                                                         1     3.869
## request_tele                                                   1     4.450
## request_resp                                                   1     2.611
## request_mrsa                                                   1    14.776
## request_vre                                                    1     8.546
## request_cdiff                                                  1     6.112
## cut2(elixhauser_hospital, g = 3)                               2    71.619
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     4   245.095
## as.factor(callout_month)                                      11     5.600
## as.factor(callout_year)                                        6     4.835
## as.factor(callout_dayofweek)                                   6    14.780
## MED_SERVICE                                                    1     2.779
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")   3     3.920
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  2     0.126
##                                                                  RSS
## <none>                                                        4451.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   4466.3
## cut2(oasis, g = 3)                                            4459.9
## cut2(age, g = 3)                                              4461.2
## female                                                        4455.4
## request_tele                                                  4456.0
## request_resp                                                  4454.2
## request_mrsa                                                  4466.3
## request_vre                                                   4460.1
## request_cdiff                                                 4457.7
## cut2(elixhauser_hospital, g = 3)                              4523.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    4696.7
## as.factor(callout_month)                                      4457.2
## as.factor(callout_year)                                       4456.4
## as.factor(callout_dayofweek)                                  4466.3
## MED_SERVICE                                                   4454.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  4455.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 4451.7
##                                                                   AIC
## <none>                                                        -6475.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   -6447.1
## cut2(oasis, g = 3)                                            -6462.3
## cut2(age, g = 3)                                              -6459.6
## female                                                        -6469.4
## request_tele                                                  -6468.2
## request_resp                                                  -6472.0
## request_mrsa                                                  -6447.1
## request_vre                                                   -6459.8
## request_cdiff                                                 -6464.8
## cut2(elixhauser_hospital, g = 3)                              -6333.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    -5993.5
## as.factor(callout_month)                                      -6485.9
## as.factor(callout_year)                                       -6477.4
## as.factor(callout_dayofweek)                                  -6457.1
## MED_SERVICE                                                   -6471.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  -6473.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) -6479.1
## Single term deletions
## 
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.00,12.00)")
##                                                               Df Sum of Sq
## <none>                                                                    
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")    1    14.748
## cut2(oasis, g = 3)                                             2     8.292
## cut2(age, g = 3)                                               2     9.628
## female                                                         1     3.869
## request_tele                                                   1     4.450
## request_resp                                                   1     2.611
## request_mrsa                                                   1    14.776
## request_vre                                                    1     8.546
## request_cdiff                                                  1     6.112
## cut2(elixhauser_hospital, g = 3)                               2    71.619
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                     4   245.095
## as.factor(callout_month)                                      11     5.600
## as.factor(callout_year)                                        6     4.835
## as.factor(callout_dayofweek)                                   6    14.780
## MED_SERVICE                                                    1     2.779
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")   3     3.920
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))  2     0.126
##                                                                  RSS
## <none>                                                        4451.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   4466.3
## cut2(oasis, g = 3)                                            4459.9
## cut2(age, g = 3)                                              4461.2
## female                                                        4455.4
## request_tele                                                  4456.0
## request_resp                                                  4454.2
## request_mrsa                                                  4466.3
## request_vre                                                   4460.1
## request_cdiff                                                 4457.7
## cut2(elixhauser_hospital, g = 3)                              4523.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    4696.7
## as.factor(callout_month)                                      4457.2
## as.factor(callout_year)                                       4456.4
## as.factor(callout_dayofweek)                                  4466.3
## MED_SERVICE                                                   4454.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  4455.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 4451.7
##                                                                   AIC
## <none>                                                        -6475.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   -6447.1
## cut2(oasis, g = 3)                                            -6462.3
## cut2(age, g = 3)                                              -6459.6
## female                                                        -6469.4
## request_tele                                                  -6468.2
## request_resp                                                  -6472.0
## request_mrsa                                                  -6447.1
## request_vre                                                   -6459.8
## request_cdiff                                                 -6464.8
## cut2(elixhauser_hospital, g = 3)                              -6333.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    -5993.5
## as.factor(callout_month)                                      -6485.9
## as.factor(callout_year)                                       -6477.4
## as.factor(callout_dayofweek)                                  -6457.1
## MED_SERVICE                                                   -6471.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  -6473.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) -6479.1
##                                                                Pr(>Chi)
## <none>                                                                 
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   3.836e-08
## cut2(oasis, g = 3)                                            0.0002025
## cut2(age, g = 3)                                              5.152e-05
## female                                                        0.0048326
## request_tele                                                  0.0025123
## request_resp                                                  0.0206083
## request_mrsa                                                  3.722e-08
## request_vre                                                   2.827e-05
## request_cdiff                                                 0.0003984
## cut2(elixhauser_hospital, g = 3)                              < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    < 2.2e-16
## as.factor(callout_month)                                      0.4030458
## as.factor(callout_year)                                       0.1279656
## as.factor(callout_dayofweek)                                  3.452e-05
## MED_SERVICE                                                   0.0169302
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  0.0450719
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.8784589
##                                                                  
## <none>                                                           
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   ***
## cut2(oasis, g = 3)                                            ***
## cut2(age, g = 3)                                              ***
## female                                                        ** 
## request_tele                                                  ** 
## request_resp                                                  *  
## request_mrsa                                                  ***
## request_vre                                                   ***
## request_cdiff                                                 ***
## cut2(elixhauser_hospital, g = 3)                              ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    ***
## as.factor(callout_month)                                         
## as.factor(callout_year)                                          
## as.factor(callout_dayofweek)                                  ***
## MED_SERVICE                                                   *  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  *  
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))"
## [1] 0.8784589
## Single term deletions
## 
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) + 
##     as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.00,12.00)")
##                                                              Df Sum of Sq
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   1    14.847
## cut2(oasis, g = 3)                                            2     8.252
## cut2(age, g = 3)                                              2     9.586
## female                                                        1     3.885
## request_tele                                                  1     4.437
## request_resp                                                  1     2.616
## request_mrsa                                                  1    14.765
## request_vre                                                   1     8.581
## request_cdiff                                                 1     6.167
## cut2(elixhauser_hospital, g = 3)                              2    71.774
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    4   245.249
## as.factor(callout_month)                                     11     5.586
## as.factor(callout_year)                                       6     4.865
## as.factor(callout_dayofweek)                                  6    14.772
## as.factor(callout_wardid == 1)                                1    36.056
## cut2(PROPFULL_BEDS, c(0.9, 1))                                2     5.588
## MED_SERVICE                                                   1     2.793
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  3     3.942
##                                                                 RSS
## <none>                                                       4451.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  4466.5
## cut2(oasis, g = 3)                                           4459.9
## cut2(age, g = 3)                                             4461.3
## female                                                       4455.6
## request_tele                                                 4456.1
## request_resp                                                 4454.3
## request_mrsa                                                 4466.5
## request_vre                                                  4460.3
## request_cdiff                                                4457.9
## cut2(elixhauser_hospital, g = 3)                             4523.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4696.9
## as.factor(callout_month)                                     4457.3
## as.factor(callout_year)                                      4456.6
## as.factor(callout_dayofweek)                                 4466.5
## as.factor(callout_wardid == 1)                               4487.8
## cut2(PROPFULL_BEDS, c(0.9, 1))                               4457.3
## MED_SERVICE                                                  4454.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4455.6
##                                                                  AIC
## <none>                                                       -6479.1
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  -6450.7
## cut2(oasis, g = 3)                                           -6466.2
## cut2(age, g = 3)                                             -6463.4
## female                                                       -6473.1
## request_tele                                                 -6472.0
## request_resp                                                 -6475.7
## request_mrsa                                                 -6450.8
## request_vre                                                  -6463.5
## request_cdiff                                                -6468.4
## cut2(elixhauser_hospital, g = 3)                             -6336.9
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   -5996.9
## as.factor(callout_month)                                     -6489.6
## as.factor(callout_year)                                      -6481.1
## as.factor(callout_dayofweek)                                 -6460.8
## as.factor(callout_wardid == 1)                               -6407.4
## cut2(PROPFULL_BEDS, c(0.9, 1))                               -6471.6
## MED_SERVICE                                                  -6475.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6477.0
##                                                               Pr(>Chi)    
## <none>                                                                    
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  3.459e-08 ***
## cut2(oasis, g = 3)                                           0.0002110 ***
## cut2(age, g = 3)                                             5.379e-05 ***
## female                                                       0.0047483 ** 
## request_tele                                                 0.0025488 ** 
## request_resp                                                 0.0204856 *  
## request_mrsa                                                 3.769e-08 ***
## request_vre                                                  2.725e-05 ***
## request_cdiff                                                0.0003748 ***
## cut2(elixhauser_hospital, g = 3)                             < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   < 2.2e-16 ***
## as.factor(callout_month)                                     0.4053529    
## as.factor(callout_year)                                      0.1253989    
## as.factor(callout_dayofweek)                                 3.479e-05 ***
## as.factor(callout_wardid == 1)                               < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                               0.0032368 ** 
## MED_SERVICE                                                  0.0166471 *  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0441913 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_month)"
## [1] 0.4053529
## Single term deletions
## 
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_dayofweek) + 
##     as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9, 
##     1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12, 
##     19)), "[ 7.00,12.00)")
##                                                              Df Sum of Sq
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   1    15.090
## cut2(oasis, g = 3)                                            2     8.310
## cut2(age, g = 3)                                              2     9.506
## female                                                        1     3.926
## request_tele                                                  1     4.299
## request_resp                                                  1     2.551
## request_mrsa                                                  1    15.020
## request_vre                                                   1     8.678
## request_cdiff                                                 1     6.256
## cut2(elixhauser_hospital, g = 3)                              2    72.098
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    4   244.892
## as.factor(callout_year)                                       6     4.938
## as.factor(callout_dayofweek)                                  6    15.319
## as.factor(callout_wardid == 1)                                1    35.756
## cut2(PROPFULL_BEDS, c(0.9, 1))                                2     5.048
## MED_SERVICE                                                   1     2.838
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  3     3.713
##                                                                 RSS
## <none>                                                       4457.3
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  4472.4
## cut2(oasis, g = 3)                                           4465.6
## cut2(age, g = 3)                                             4466.8
## female                                                       4461.2
## request_tele                                                 4461.6
## request_resp                                                 4459.8
## request_mrsa                                                 4472.3
## request_vre                                                  4466.0
## request_cdiff                                                4463.5
## cut2(elixhauser_hospital, g = 3)                             4529.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4702.2
## as.factor(callout_year)                                      4462.2
## as.factor(callout_dayofweek)                                 4472.6
## as.factor(callout_wardid == 1)                               4493.0
## cut2(PROPFULL_BEDS, c(0.9, 1))                               4462.3
## MED_SERVICE                                                  4460.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4461.0
##                                                                  AIC
## <none>                                                       -6489.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  -6460.7
## cut2(oasis, g = 3)                                           -6476.6
## cut2(age, g = 3)                                             -6474.2
## female                                                       -6483.6
## request_tele                                                 -6482.8
## request_resp                                                 -6486.4
## request_mrsa                                                 -6460.9
## request_vre                                                  -6473.9
## request_cdiff                                                -6478.8
## cut2(elixhauser_hospital, g = 3)                             -6347.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   -6008.8
## as.factor(callout_year)                                      -6491.5
## as.factor(callout_dayofweek)                                 -6470.3
## as.factor(callout_wardid == 1)                               -6418.6
## cut2(PROPFULL_BEDS, c(0.9, 1))                               -6483.3
## MED_SERVICE                                                  -6485.8
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6488.0
##                                                               Pr(>Chi)    
## <none>                                                                    
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  2.728e-08 ***
## cut2(oasis, g = 3)                                           0.0002010 ***
## cut2(age, g = 3)                                             5.911e-05 ***
## female                                                       0.0045581 ** 
## request_tele                                                 0.0029951 ** 
## request_resp                                                 0.0222027 *  
## request_mrsa                                                 2.937e-08 ***
## request_vre                                                  2.482e-05 ***
## request_cdiff                                                0.0003431 ***
## cut2(elixhauser_hospital, g = 3)                             < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   < 2.2e-16 ***
## as.factor(callout_year)                                      0.1197005    
## as.factor(callout_dayofweek)                                 2.164e-05 ***
## as.factor(callout_wardid == 1)                               < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                               0.0056710 ** 
## MED_SERVICE                                                  0.0158633 *  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0547939 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "as.factor(callout_year)"
## [1] 0.1197005
## Single term deletions
## 
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, 
##     c(7, 12, 19)), "[ 7.00,12.00)")
##                                                              Df Sum of Sq
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")   1    14.596
## cut2(oasis, g = 3)                                            2     8.081
## cut2(age, g = 3)                                              2     9.483
## female                                                        1     4.071
## request_tele                                                  1     4.283
## request_resp                                                  1     2.489
## request_mrsa                                                  1    14.469
## request_vre                                                   1     8.732
## request_cdiff                                                 1     6.378
## cut2(elixhauser_hospital, g = 3)                              2    71.836
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                    4   248.341
## as.factor(callout_dayofweek)                                  6    15.868
## as.factor(callout_wardid == 1)                                1    35.367
## cut2(PROPFULL_BEDS, c(0.9, 1))                                2     3.827
## MED_SERVICE                                                   1     2.477
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")  3     3.551
##                                                                 RSS
## <none>                                                       4462.2
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  4476.8
## cut2(oasis, g = 3)                                           4470.3
## cut2(age, g = 3)                                             4471.7
## female                                                       4466.3
## request_tele                                                 4466.5
## request_resp                                                 4464.7
## request_mrsa                                                 4476.7
## request_vre                                                  4471.0
## request_cdiff                                                4468.6
## cut2(elixhauser_hospital, g = 3)                             4534.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4710.6
## as.factor(callout_dayofweek)                                 4478.1
## as.factor(callout_wardid == 1)                               4497.6
## cut2(PROPFULL_BEDS, c(0.9, 1))                               4466.0
## MED_SERVICE                                                  4464.7
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4465.8
##                                                                  AIC
## <none>                                                       -6491.5
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  -6463.7
## cut2(oasis, g = 3)                                           -6479.0
## cut2(age, g = 3)                                             -6476.1
## female                                                       -6485.2
## request_tele                                                 -6484.7
## request_resp                                                 -6488.4
## request_mrsa                                                 -6463.9
## request_vre                                                  -6475.6
## request_cdiff                                                -6480.5
## cut2(elixhauser_hospital, g = 3)                             -6349.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   -6004.5
## as.factor(callout_dayofweek)                                 -6471.1
## as.factor(callout_wardid == 1)                               -6421.4
## cut2(PROPFULL_BEDS, c(0.9, 1))                               -6487.7
## MED_SERVICE                                                  -6488.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6490.2
##                                                               Pr(>Chi)    
## <none>                                                                    
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  4.673e-08 ***
## cut2(oasis, g = 3)                                           0.0002563 ***
## cut2(age, g = 3)                                             6.119e-05 ***
## female                                                       0.0038897 ** 
## request_tele                                                 0.0030654 ** 
## request_resp                                                 0.0239719 *  
## request_mrsa                                                 5.343e-08 ***
## request_vre                                                  2.368e-05 ***
## request_cdiff                                                0.0003025 ***
## cut2(elixhauser_hospital, g = 3)                             < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   < 2.2e-16 ***
## as.factor(callout_dayofweek)                                 1.341e-05 ***
## as.factor(callout_wardid == 1)                               < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                               0.0198863 *  
## MED_SERVICE                                                  0.0243217 *  
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0637767 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "relevel(cut2(hourofcallout2, c(7, 12, 19)), \"[ 7.00,12.00)\")"
## [1] 0.06377669
## Single term deletions
## 
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == 
##     "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) + 
##     female + request_tele + request_resp + request_mrsa + request_vre + 
##     request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days, 
##     c(1, 3, 7, 28)) + as.factor(callout_dayofweek) + as.factor(callout_wardid == 
##     1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE
##                                                             Df Sum of Sq
## <none>                                                                  
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")  1    14.130
## cut2(oasis, g = 3)                                           2     8.072
## cut2(age, g = 3)                                             2     9.479
## female                                                       1     4.006
## request_tele                                                 1     4.524
## request_resp                                                 1     2.404
## request_mrsa                                                 1    14.427
## request_vre                                                  1     8.755
## request_cdiff                                                1     6.430
## cut2(elixhauser_hospital, g = 3)                             2    72.409
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                   4   250.962
## as.factor(callout_dayofweek)                                 6    15.579
## as.factor(callout_wardid == 1)                               1    35.176
## cut2(PROPFULL_BEDS, c(0.9, 1))                               2     3.973
## MED_SERVICE                                                  1     2.577
##                                                                RSS     AIC
## <none>                                                      4465.8 -6490.2
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4479.9 -6463.4
## cut2(oasis, g = 3)                                          4473.8 -6477.7
## cut2(age, g = 3)                                            4475.2 -6474.9
## female                                                      4469.8 -6484.0
## request_tele                                                4470.3 -6483.0
## request_resp                                                4468.2 -6487.3
## request_mrsa                                                4480.2 -6462.8
## request_vre                                                 4474.5 -6474.3
## request_cdiff                                               4472.2 -6479.1
## cut2(elixhauser_hospital, g = 3)                            4538.2 -6347.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  4716.7 -5998.5
## as.factor(callout_dayofweek)                                4481.3 -6470.4
## as.factor(callout_wardid == 1)                              4500.9 -6420.5
## cut2(PROPFULL_BEDS, c(0.9, 1))                              4469.7 -6486.1
## MED_SERVICE                                                 4468.3 -6487.0
##                                                              Pr(>Chi)    
## <none>                                                                   
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 7.724e-08 ***
## cut2(oasis, g = 3)                                          0.0002606 ***
## cut2(age, g = 3)                                            6.187e-05 ***
## female                                                      0.0042022 ** 
## request_tele                                                0.0023487 ** 
## request_resp                                                0.0265649 *  
## request_mrsa                                                5.648e-08 ***
## request_vre                                                 2.326e-05 ***
## request_cdiff                                               0.0002875 ***
## cut2(elixhauser_hospital, g = 3)                            < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))                  < 2.2e-16 ***
## as.factor(callout_dayofweek)                                1.759e-05 ***
## as.factor(callout_wardid == 1)                              < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1))                              0.0171892 *  
## MED_SERVICE                                                 0.0216504 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "request_resp"
## [1] 0.02656487
    log(los_post_icu_days + 1)
    Estimate CI p
(Intercept)   4.43 4.08 – 4.82 <.001
I(cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]”)   0.88 0.84 – 0.92 <.001
cut2(oasis, g = 3)
[27,33)   1.06 1.02 – 1.09 .003
[33,62]   1.08 1.04 – 1.12 <.001
cut2(age, g = 3)
[55.8,73.4)   1.08 1.05 – 1.12 <.001
[73.4,91.4]   1.04 1.00 – 1.08 .076
female   1.04 1.01 – 1.07 .004
request_tele   1.05 1.02 – 1.08 .002
request_resp   1.14 1.01 – 1.27 .027
request_mrsa   1.13 1.08 – 1.18 <.001
request_vre   1.16 1.08 – 1.24 <.001
request_cdiff   1.13 1.06 – 1.21 <.001
cut2(elixhauser_hospital, g = 3)
[ 1, 7)   1.12 1.08 – 1.16 <.001
[ 7,31]   1.25 1.21 – 1.30 <.001
cut2(los_pre_callout_days, c(1, 3, 7, 28))
[ 1.000, 3.000)   1.10 1.06 – 1.14 <.001
[ 3.000, 7.000)   1.34 1.28 – 1.40 <.001
[ 7.000, 28.000)   1.73 1.64 – 1.83 <.001
[ 28.000,110.022]   2.10 1.65 – 2.66 <.001
as.factor(callout_dayofweek)
monday   0.89 0.84 – 0.94 <.001
saturday   0.97 0.92 – 1.03 .334
sunday   0.94 0.88 – 0.99 .029
thursday   0.96 0.91 – 1.02 .177
tuesday   0.88 0.84 – 0.93 <.001
wednesday   0.94 0.89 – 0.99 .022
as.factor(callout_wardid == 1) (TRUE)   0.83 0.80 – 0.87 <.001
cut2(PROPFULL_BEDS, c(0.9, 1))
[0.900,1.000)   1.00 0.96 – 1.04 .963
[1.000,1.093]   0.94 0.89 – 0.99 .018
MED_SERVICE   0.93 0.88 – 0.99 .022
Observations   9140
##                                                                       
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -0.131
## cut2(oasis, g = 3)[27,33)                                        0.054
## cut2(oasis, g = 3)[33,62]                                        0.074
## cut2(age, g = 3)[55.8,73.4)                                      0.080
## cut2(age, g = 3)[73.4,91.4]                                      0.035
## female                                                           0.042
## request_tele                                                     0.047
## request_resp                                                     0.127
## request_mrsa                                                     0.120
## request_vre                                                      0.148
## request_cdiff                                                    0.126
## cut2(elixhauser_hospital, g = 3)[  1, 7)                         0.109
## cut2(elixhauser_hospital, g = 3)[  7,31]                         0.226
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)      0.094
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)      0.290
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)      0.549
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022]      0.741
## as.factor(callout_dayofweek)monday                              -0.115
## as.factor(callout_dayofweek)saturday                            -0.028
## as.factor(callout_dayofweek)sunday                              -0.065
## as.factor(callout_dayofweek)thursday                            -0.037
## as.factor(callout_dayofweek)tuesday                             -0.126
## as.factor(callout_dayofweek)wednesday                           -0.062
## as.factor(callout_wardid == 1)TRUE                              -0.185
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                     -0.001
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                     -0.062
## MED_SERVICETRUE                                                 -0.068
##                                                                  2.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -0.179
## cut2(oasis, g = 3)[27,33)                                        0.018
## cut2(oasis, g = 3)[33,62]                                        0.037
## cut2(age, g = 3)[55.8,73.4)                                      0.044
## cut2(age, g = 3)[73.4,91.4]                                     -0.004
## female                                                           0.013
## request_tele                                                     0.017
## request_resp                                                     0.015
## request_mrsa                                                     0.077
## request_vre                                                      0.079
## request_cdiff                                                    0.058
## cut2(elixhauser_hospital, g = 3)[  1, 7)                         0.074
## cut2(elixhauser_hospital, g = 3)[  7,31]                         0.190
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)      0.058
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)      0.247
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)      0.495
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022]      0.503
## as.factor(callout_dayofweek)monday                              -0.169
## as.factor(callout_dayofweek)saturday                            -0.086
## as.factor(callout_dayofweek)sunday                              -0.123
## as.factor(callout_dayofweek)thursday                            -0.091
## as.factor(callout_dayofweek)tuesday                             -0.180
## as.factor(callout_dayofweek)wednesday                           -0.115
## as.factor(callout_wardid == 1)TRUE                              -0.228
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                     -0.040
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                     -0.113
## MED_SERVICETRUE                                                 -0.126
##                                                                 97.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -0.083
## cut2(oasis, g = 3)[27,33)                                        0.090
## cut2(oasis, g = 3)[33,62]                                        0.112
## cut2(age, g = 3)[55.8,73.4)                                      0.116
## cut2(age, g = 3)[73.4,91.4]                                      0.074
## female                                                           0.071
## request_tele                                                     0.077
## request_resp                                                     0.239
## request_mrsa                                                     0.164
## request_vre                                                      0.217
## request_cdiff                                                    0.194
## cut2(elixhauser_hospital, g = 3)[  1, 7)                         0.145
## cut2(elixhauser_hospital, g = 3)[  7,31]                         0.262
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)      0.129
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)      0.334
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)      0.602
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022]      0.979
## as.factor(callout_dayofweek)monday                              -0.061
## as.factor(callout_dayofweek)saturday                             0.029
## as.factor(callout_dayofweek)sunday                              -0.007
## as.factor(callout_dayofweek)thursday                             0.017
## as.factor(callout_dayofweek)tuesday                             -0.072
## as.factor(callout_dayofweek)wednesday                           -0.009
## as.factor(callout_wardid == 1)TRUE                              -0.142
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                      0.038
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                     -0.011
## MED_SERVICETRUE                                                 -0.010
##                                                                      
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.000
## cut2(oasis, g = 3)[27,33)                                       0.003
## cut2(oasis, g = 3)[33,62]                                       0.000
## cut2(age, g = 3)[55.8,73.4)                                     0.000
## cut2(age, g = 3)[73.4,91.4]                                     0.076
## female                                                          0.004
## request_tele                                                    0.002
## request_resp                                                    0.027
## request_mrsa                                                    0.000
## request_vre                                                     0.000
## request_cdiff                                                   0.000
## cut2(elixhauser_hospital, g = 3)[  1, 7)                        0.000
## cut2(elixhauser_hospital, g = 3)[  7,31]                        0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  1.000,  3.000)     0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  3.000,  7.000)     0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[  7.000, 28.000)     0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022]     0.000
## as.factor(callout_dayofweek)monday                              0.000
## as.factor(callout_dayofweek)saturday                            0.334
## as.factor(callout_dayofweek)sunday                              0.029
## as.factor(callout_dayofweek)thursday                            0.177
## as.factor(callout_dayofweek)tuesday                             0.000
## as.factor(callout_dayofweek)wednesday                           0.022
## as.factor(callout_wardid == 1)TRUE                              0.000
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)                     0.963
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]                     0.018
## MED_SERVICETRUE                                                 0.022

Answer 6: After adjusting for potential confounders, There appears to be some evidence that those with long delays have about 12% shorter post ICU LOS.